Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications (Remote Sensing Applications Series)

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Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications (Remote Sensing Applications Series)

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Advances in Environmental Remote Sensing Sensors, Algorithms, and Applications

Taylor & Francis Series in Remote Sensing Applications 6HULHV (GLWRU


Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, edited by Qihao Weng Remote Sensing of Coastal Environments, edited by Yeqiao Wang Remote Sensing of Global Croplands for Food Security, edited by Prasad S. Thenkabail, John G. Lyon, Hugh Turral, and Chandashekhar M. Biradar Global Mapping of Human Settlement: Experiences, Data Sets, and Prospects, edited by Paolo Gamba and Martin Herold Hyperspectral Remote Sensing: Principles and Applications, Marcus Borengasser, William S. Hungate, and Russell Watkins Remote Sensing of Impervious Surfaces, Qihao Weng Multispectral Image Analysis Using the Object-Oriented Paradigm, Kumar Navulur

Advances in Environmental Remote Sensing Sensors, Algorithms, and Applications

Edited by

Qihao Weng

Boca Raton London New York

CRC Press is an imprint of the Taylor & Francis Group, an informa business

MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software.

CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2011 by Taylor and Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number-13: 978-1-4200-9181-6 (Ebook-PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access (http:// or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at and the CRC Press Web site at

Contents Acknowledgments����������������������������������������������������������������������������������������������������������������������� vii Editor�����������������������������������������������������������������������������������������������������������������������������������������������ix Contributors������������������������������������������������������������������������������������������������������������������������������������xi Introduction���������������������������������������������������������������������������������������������������������������������������������..xv

Section I Sensors, Systems, and Platforms 1. Remote Sensing of Vegetation with Landsat Imagery.................................................... 3 Conghe Song, Joshua M. Gray, and Feng Gao 2. Review of Selected Moderate-Resolution Imaging Spectroradiometer Algorithms, Data Products, and Applications................................................................. 31 Yang Shao, Gregory N. Taff, and Ross S. Lunetta 3. Lidar Remote Sensing........................................................................................................... 57 Sorin C. Popescu 4. Impulse Synthetic Aperture Radar.................................................................................... 85 Giorgio Franceschetti and James Z. Tatoian 5. Hyperspectral Remote Sensing of Vegetation Bioparameters................................... 101 Ruiliang Pu and Peng Gong 6. Thermal Remote Sensing of Urban Areas: Theoretical Backgrounds and Case Studies.................................................................................................................. 143 Qihao Weng

Section II Algorithms and Techniques 7. Atmospheric Correction Methods for Optical Remote Sensing Imagery of Land................................................................................................................... 161 Rudolf Richter 8. Three-Dimensional Geometric Correction of Earth Observation Satellite Data........................................................................................................................ 173 Thierry Toutin 9. Remote Sensing Image Classification............................................................................. 219 Dengsheng Lu, Qihao Weng, Emilio Moran, Guiying Li, and Scott Hetrick




10. Object-Based Image Analysis for Vegetation Mapping and Monitoring................ 241 Thomas Blaschke, Kasper Johansen, and Dirk Tiede 11. Land-Use and Land-Cover Change Detection............................................................... 273 Dengsheng Lu, Emilio Moran, Scott Hetrick, and Guiying Li

Section III Environmental Applications-Vegetation 12. Remote Sensing of Ecosystem Structure and Function............................................... 291 Alfredo R. Huete and Edward P. Glenn 13. Remote Sensing of Live Fuel Moisture........................................................................... 321 Stephen R. Yool 14. Forest Change Analysis Using Time-Series Landsat Observations......................... 339 Chengquan Huang 15. Satellite-Based Modeling of Gross Primary Production of Terrestrial Ecosystems........................................................................................................ 367 Xiangming Xiao, Huimin Yan, Joshua Kalfas, and Qingyuan Zhang 16. Global Croplands and Their Water Use from Remote Sensing and Nonremote Sensing Perspectives..................................................................................... 383 Prasad S. Thenkabail, Munir A. Hanjra, Venkateswarlu Dheeravath, and Muralikrishna Gumma

Section IV Environmental Applications: Air, Water, and Land 17. Remote Sensing of Aerosols from Space: A Review of Aerosol Retrieval Using the Moderate-Resolution Imaging Spectroradiometer.................................... 423 Man Sing Wong and Janet Nichol 18. Remote Estimation of Chlorophyll-a Concentration in Inland, Estuarine, and Coastal Waters.......................................................................................... 439 Anatoly A. Gitelson, Daniela Gurlin, Wesley J. Moses, and Yosef Z. Yacobi 19. Retrievals of Turbulent Heat Fluxes and Surface Soil Water Content by Remote Sensing................................................................................................................... 469 George P. Petropoulos and Toby N. Carlson 20. Remote Sensing of Urban Biophysical Environments................................................. 503 Qihao Weng 21. Development of the USGS National Land-Cover Database over Two Decades..... 525 George Xian, Collin Homer, and Limin Yang Index..............................................................................................................................................545

Acknowledgments I extend my heartfelt thanks to all the contributors of this book for making this endeavor possible. Moreover, I offer my deepest appreciation to all the reviewers, who have taken precious time from their busy schedules to review the chapters submitted for this book. Finally, I am indebted to my family for their enduring love and support. It is my hope that the publication of this book will facilitate students to understand the state-of-the art knowledge of environmental remote sensing and to provide researchers with an update on the newest development in many sub-fields of this dynamic field. The reviewers of the chapters of this book are listed here in alphabetical order: Thomas Blaschke Hubo Cai Toby Carlson Paolo Gamba Anatoly Gitelson Chengquan Huang Stefaan Lhermitte Lin Li Desheng Liu Hua Liu Dengsheng Lu Janet Nichol Ruiliang Pu

Dale Quattrochi Yang Shao Conghe Song Junmei Tang Xiaohua Tong Guangxin Wang George Xian Xiangming Xiao Jian-sheng Yang Ping Yang Zhengwei Yang Fei Yuan Yuyu Zhou


Editor Dr. Qihao Weng is a professor of geography and the director of the Center for Urban and Environmental Change at Indiana State University. From 2008 to 2009, he visited the National Aeronautics and Space Administration (NASA) as a senior research fellow. He is also a guest/adjunct professor at Wuhan University and Beijing Normal University, and a guest research scientist at the Beijing Meteorological Bureau in China. His research focuses on remote sensing and GIS analysis of urban environmental systems, land-use and land-cover change, urbanization impacts, and human– environment interactions. Dr. Weng is the author of more than 120 peer-reviewed journal articles and other publications and three books (Urban Remote Sensing, 2006, CRC Press; Remote Sensing of Impervious Surfaces, 2007, CRC Press; and Remote Sensing and GIS Integration: Theories, Methods, and Applications, 2009, McGraw-Hill Professional). He has been the recipient of some significant awards, including the Robert E. Altenhofen Memorial Scholarship Award (1998) from the American Society for Photogrammetry and Remote Sensing (ASPRS), the Best Student-Authored Paper Award from the International Geographic Information Foundation (1999), the Theodore Dreiser Distinguished Research Award from Indiana State University (2006), a NASA senior fellowship (2008), and the 2010 Erdas Award for Best Scientific Paper in Remote Sensing from ASPRS (first place). Dr. Weng has worked extensively with optical and thermal remote sensing data, with research support from the National Science Foundation (NSF), NASA, USGS, the U.S. Agency for International Development (USAID), the National Geographic Society, and the Indiana Department of Natural Resources. Professionally, Dr.  Weng was a national director of ASPRS (2007–2010). He also serves as an associate ­editor of ISPRS Journal of Photogrammetry and Remote Sensing, and is the series editor for both the Taylor & Francis series in remote sensing applications, and the McGraw-Hill series in GIS&T.


Contributors Thomas Blaschke Z_GIS Centre for Geoinformatics and Department for Geography and Geology University of Salzburg Salzburg, Austria Toby N. Carlson Department of Meteorology Penn State University University Park, Pennsylvania Venkateswarlu Dheeravath World Food Program United Nations Joint Logistic Center Juba, South Sudan, Sudan Giorgio Franceschetti Eureka Aerospace Pasadena, California Feng Gao NASA Goddard Space Flight Center Greenbelt, Maryland Anatoly A. Gitelson CALMIT, School of Natural Resources University of Nebraska Lincoln, Nebraska Edward P. Glenn Department of Soil, Water, and Environmental Science University of Arizona Tucson, Arizona Peng Gong Department of Environmental Science, Policy, and Management University of California Berkeley, California Joshua M. Gray Department of Geography University of North Carolina Chapel Hill, North Carolina

Muralikrishna Gumma International Rice Research Center Manila, Philippines Daniela Gurlin CALMIT, School of Natural Resources University of Nebraska Lincoln, Nebraska Munir A. Hanjra International Centre of Water for Food Security Charles Stuart University Wagga Wagga, NSW, Australia Scott Hetrick Anthropological Center for Training and Research on Global Environmental Change Indiana University Bloomington, Indiana Collin Homer USGS Earth Resources Observation and Science Center Sioux Falls, South Dakota Chengquan Huang Department of Geography University of Maryland College Park, Maryland Alfredo R. Huete Department of Plant Functional Biology and Climate Change Cluster University of Technology Sydney, NSW, Australia Kasper Johansen Centre for Spatial Environmental Research, School of Geography, Planning and Environmental Management University of Queensland Brisbane, Australia xi



Joshua Kalfas Department of Botany and Microbiology Center for Spatial Analysis University of Oklahoma Norman, Oklahoma

Sorin C. Popescu Department of Ecosystem Science and Management Texas A&M University College Station, Texas

Guiying Li Anthropological Center for Training and Research on Global Environmental Change Indiana University Bloomington, Indiana

Ruiliang Pu Department of Geography University of South Florida Tampa, Florida

Dengsheng Lu Anthropological Center for Training and Research on Global Environmental Change Indiana University Bloomington, Indiana Ross S. Lunetta National Exposure Research Laboratory (NERL) U.S. Environmental Protection Agency Research Triangle Park, North Carolina Emilio Moran Anthropological Center for Training and Research on Global Environmental Change Indiana University Bloomington, Indiana Wesley J. Moses Naval Research Laboratory Washington, DC Janet Nichol Department of Land Surveying and ­Geo­-Informatics Hong Kong Polytechnic University Hunghom, Kowloon, Hong Kong George P. Petropoulos Regional Analysis Division Foundation for Research and Technology Hellas Institute of Applied and Computational Mathematics Heraklion, Crete, Greece

Rudolf Richter DLR–German Aerospace Center DRD–Remote Sensing Data Center Wessling, Germany Yang Shao National Research Council U.S. Environmental Protection Agency Research Triangle Park, North Carolina Conghe Song Department of Geography University of North Carolina Chapel Hill, North Carolina Gregory N. Taff Department of Earth Sciences University of Memphis Memphis, Tennessee James Z. Tatoian Eureka Aerospace Pasadena, California Prasad S. Thenkabail Southwest Geographic Science Center U.S. Geological Survey Flagstaff, Arizona Dirk Tiede Z_GIS Centre for Geoinformatics and Department for Geography and Geology University of Salzburg Salzburg, Austria



Thierry Toutin Natural Resources Canada Canada Centre for Remote Sensing Ottawa, Ontario, Canada Qihao Weng Department of Geography Center for Urban and Environmental Change Indiana State University Terre Haute, Indiana Man Sing Wong Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic University Hunghom, Kowloon, Hong Kong George Xian ARTS/USGS Earth Resources Observation and Science Center Sioux Falls, South Dakota Xiangming Xiao Department of Botany and Microbiology, College of Arts and Sciences Center for Spatial Analysis, College of Atmospheric and Geographic Science University of Oklahoma Norman, Oklahoma

Yosef Z. Yacobi Israel Oceanographic & Limnological Research Yigal Allon Kinneret Limnological Laboratory Migdal, Israel Huimin Yan Institute of Geographic Science and Natural Resources Research Chinese Academy of Sciences Beijing, China Limin Yang USGS Earth Resources Observation and Science Center Sioux Falls, South Dakota Stephen R. Yool Department of Geography and Development University of Arizona Tucson, Arizona Qingyuan Zhang Goddard Space Flight Center NASA Greenbelt, Maryland

Introduction to Recent Advances in Remote Sensing of the Environment

Aims and Scope The main purpose of compiling such a book is to provide an authoritative supplementary text for upper-division undergraduate and graduate students, who may have chosen a textbook from a variety of choices in the market. This book collects two types of articles: (1) comprehensive review articles from leading authorities to examine the developments in concepts, methods, techniques, and applications in a subfield of environmental remote sensing, and (2) focused review articles regarding the latest developments in a hot topic with one to two concise case studies. Because of the nature of articles collected, this book can also serve as a good reference book for researchers, scientists, engineers, and policymakers who wish to keep up with new developments in environmental remote sensing.

Synopsis of the Book This book is divided into four sections. Section I deals with various sensors, systems, or sensing using different regions of wavelengths. Section II exemplifies recent advances in algorithms and techniques, specifically in image preprocessing and thematic information extraction. Section III focuses on remote sensing of vegetation and related features of the Earth’s surface. Finally, Section IV examines developments in the remote sensing of air, water, and other terrestrial features. The chapters in Section I provide a comprehensive overview of some important sensors and remote sensing systems, with the exception of Chapter 5. By reviewing key concepts and methods and illustrating practical uses of particular sensors/sensing systems, these chapters provide insights into the most recent developments and trends in remote sensing and further identify the major existing problems of these trends. These remote sensing systems utilize visible, reflected infrared, thermal infrared, and microwave spectra, and include both passive and active sensors. In Chapter 1, Song and his colleagues evaluate one of the longest remote sensing programs in the world, that is, the U.S. Landsat program, and discuss its applications in vegetation studies. With a mission of long-term monitoring of vegetation and terrestrial features, Landsat has built up a glorious history. The remote sensing literature is filled with a large number of articles in vegetation classification and change detection. However, remote sensing of vegetation remains a great challenge, especially the sensing of biophysical parameters such as leaf area index (LAI), biomass, and forest successional stages (Song, Gray, and Gao 2010). A remarkable strength of the Landsat program is its time-series data, especially when considering the addition xv


Introduction to Recent Advances in Remote Sensing of the Environment

of the upcoming Landsat Data Continuity Mission (LDCM); however, these data are not a panacea for ­vegetation studies. Song, Gray, and Gao (2010) suggest that the synergistic use of data from other remote sensors may provide complimentary vegetation information to Landsat data, such as high spatial resolution (0.11) and 4 (>0.10). Generally, the NDSI value decreases as the purity of snow pixels is reduced. In order to identify a partial snow pixel (e.g., >50%) in a forested region, Snowmap incorporates MODIS-NDVI to map the snow pixel. For instance, pixels might be labeled as snow in cases of NDVI = 0.1 approximately and NDSI 0 and t < 0, respectively. Equation 4.39 represents the impulse response of the filter function centered at ω = Ω, whose expression in the phasor domain is =

hˆΩ (t) = γ exp(− γ t )exp(i Ω t)


In order to be consistent with the usual procedure used in conventional SAR processing that utilizes I- and Q-channels, we move to the phasor domain for the continuation of our analysis. Examination of Equation 4.38 shows that

gˆ Ω (t) = g(t) ⊗ hˆΩ (t)


Impulse Synthetic Aperture Radar


where the symbol ⊗ is the convolution operator, and the hat symbol indicates the phasor quantities. Though this convolution is rather elaborate, it can be computed and examined (Eureka Aerospace 2008). Only a simplified analysis is reported hereafter in order to present and point out the basic features and qualifying parameters of polychromatic SAR. The design value of γ for the filter function is dictated by the usual choices for its relative bandwidth, 2γ/Ω. In a conventional SAR system, this relative bandwidth is usually between 1% and 10% in airborne and spaceborne applications. Raw polychromatic SAR data is processed, yielding a number of microwave images, which are coincident (or at least similar) to those obtainable with conventional SAR systems. For the large value of Ω = 2, this normalized bandwidth is at most 0.2, which is much smaller than the signal bandwidth of 2 given in Equation 4.33. The conclusion is that

GΩ (ω ) = G (ω ) H (ω − Ω) ≅ G (Ω) H (ω − Ω)


gˆ Ω (t) ∝ hˆΩ (t) = γ exp(− γ t )exp(i Ω t)


so that

The signal represented by Equation 4.43 is proportional to the signal that would be transmitted if only the subbands around Ω were used. The return signal scattered by a point target at range r is proportional to exp(− γ t − t ′ )exp[i Ω(t − t ′)], where t′ = 2r/c. An estimate of the attainable range resolution is obtained by compressing the raw data, implemented by removing the exp(iΩt) term via heterodyning and evaluating the convolution

sˆ(t) = exp(− γ t − t ′ )exp(− i Ωt ′) ⊗ exp(− γ t )


For t−t′ = η ≥ 0, we get η 0  exp( − γ [ η − τ ])e xp( γτ )d τ + exp[− γ ( η − τ)]exp(− γτ)d τ   ∫ ∫ +∞  −∞  0  ∫ exp(− γ η − τ )exp(− γ τ )d τ =  +∞ −∞ + exp[ γ ( η − τ)]exp(− γτ)d τ   ∫   η  exp(− γη) (− γη) 1 + γη (4.45) + η exp(− γξ) + = exp(− γη) = 2γ γ 2γγ

and we get the same result by substituting η → |η| when η ≤ 0. We conclude that the processed signal is given, except for a multiplicative constant, by

sˆ(t) =

1 + γ t − t′ exp (− γ t − t ′ ) exp(− i Ωt ′) γ


The modulus of the signal attains its maximum value, sˆM = 1 γ, at t  −  t ′ = 0, steadily decreases for |t − t′|> 0, and exhibits two inflection points at t − t′ = ± 1/γ, where its value is 2 sˆM exp(−1) = 0�736 sˆM ≅ 0�707 sˆM . The latter result provides an estimate of the effective


Advances in Environmental Remote Sensing

Optical image

ImpSAR image

fΩ = 4 GHz

fΩ = 6 GHz

fΩ = 8 GHz

fΩ = 10 GHz

Figure 4.7 A comparison between impulse synthetic aperture radar (ImpSAR) and polychromatic synthetic aperture radar (SAR) images. The figure on the top left is the optical image of the target, and the second one is the ImpSAR image. The other figures are polychromatic SAR images, with the “chopped” bandwidth set to 3 GHz and centered at the frequencies indicated.

pulsewidth, 2/γ, centered at t = t′, of the compressed signal, leading to the evaluation of the attainable range resolution. Referring to nonnormalized quantities, we get

∆r =

4c Ω λ 2π 2 λ Ω 2 λ 2 2c = =4 Ω = = 1�27 Ω γ (2 γ ) Ω (2 γ ) Ω π (2 γ ) Ω (2 γ ) Ω


where λ Ω = 2πc/Ω is the wavelength of the center (carrier) angular frequency of the “chopped” bandwidth signal. The result given by Equation 4.47 mirrors that of the range resolution attainable by a conventional chirped SAR system, namely (λ/2)/(Δ f / f ), where f → Ω/2π and Δ f → 2γ are the carrier frequency and chirp bandwidth, respectively. An example of polychromatic SAR imaging is depicted in Figure 4.7. The difference between the ImpSAR image, which uses the entire 12-GHz bandwidth of the radiated signal, and polychromatic SAR images, each limited to a 3-GHz bandwidth centered at the frequencies indicated, is clearly pronounced. Different responses of the large target to different frequencies are also observed—a result that is open to further analysis.

4.5  Conclusions In this chapter, two novel concepts of SAR imaging, namely impulse SAR and polychromatic SAR, were discussed at length. The theoretical foundation of the two systems has been presented and validated by experimental results. These two sensors exhibit promising

Impulse Synthetic Aperture Radar


features and have a wide range of potential applications where they have distinct advantages over conventional microwave imaging systems. Impulse SAR has a number of attractive features. Its high range resolution is easily achievable with very short carrierless pulses; the absence of phase infers the absence of the grating lobes that are convenient for stereometric applications; signal processing is very fast, because it is directly implemented in time domain using the shift-and-add procedure. Finally, compared to conventional SAR systems, ImpSAR hardware is simple, and the system design and integration are straightforward. Moreover, its wide bandwidth allows an easy extension to polychromatic SAR. Polychromatic SAR has the useful capability of generating multiple images simultaneously, which is of particular importance to the target detection and identification process. It is implemented purely in software (as it runs on existing ImpSAR hardware), and its utility can be easily extended to low-frequency ranges. This feature is particularly attractive as it extends SAR utility to ground-penetrating applications, including detection and identification of buried mines, unexploded ordnance, improvised explosive devices, pipes, and underground structures. There is no doubt that, for the time being, the use of these sensors is limited to ground and airborne operations. However, this issue is only due to the limits of attainable radiating power using available solid-state pulsers and is expected to be solved with the increasing demand for impulse imaging technology. Additional theoretical analysis is required to improve these systems, in particular, a deeper examination of the scattering of large bodies by very narrow pulses, when the pulse and the target are on two different spatial scales. This difference has not been explored on purpose, as it is believed that the problem should be modeled and solved directly in the time domain, without passing through the frequency domain, which is an ill-suited approach to the presented problem. This theoretical exploration is in progress.

Acknowledgments The authors would like to thank George Gibbs of the U.S. Marine Corps System Command (MARCORSYSCOM) in Quantico, Virginia, and Martin Kruger and Andre des Rosiers of the Office of Naval Research (ONR) in Arlington, Virginia for their unstinting support, encouragement, and guidance throughout the entire ImpSAR development effort.

References Amin, M., ed. 2010. Through the Wall Radar Imaging. Boca Raton, FL: CRC Press. Curlander, J. C., and R. N. McDonough. 1991. Synthetic Aperture Radar: Systems and Signal Processing (Wiley Series in Remote Sensing). New York: John Wiley & Sons. Eureka Aerospace. 2008. Polychromatic SAR: A new concept in imaging radar. In Technical Report to MARCORSYSCOM, contract M67854-07-C-1122, February 29, 2008. Fornaro, G., and F. Serafino. 2006. Imaging of single and double scatterers in urban areas via SAR tomography. IEEE Trans Geosci Remote Sens 44:3497–505.


Advances in Environmental Remote Sensing

Franceschetti, G. 1997. Electromagnetics: Theory, Techniques, and Engineering Paradigms. New York: Plenum Press. Franceschetti, G., and R. Lanari. 1999. SAR Processing Techniques. Boca Raton, FL: CRC Press. Franceschetti, G., and C. H. Papas. 1974. Pulsed antennas. IEEE Trans Antennas Propag 22:651–61. Franceschetti, G., J. Tatoian, and G. Gibbs. 2005. Timed arrays in a nutshell. IEEE Trans Antennas Propag 53(12):4073–82. Franceschetti, G., J. Tatoian, and G. Gibbs. 2009. Looking into transient scattering. In Proceedings of PIERS 2009, Xi’an, China. Cambridge, MA: The Electromagnetics Academy.

5 Hyperspectral Remote Sensing of Vegetation Bioparameters Ruiliang Pu and Peng Gong Contents 5.1 Introduction......................................................................................................................... 101 5.2 Spectral Characteristics of Typical Bioparameters......................................................... 103 5.2.1 Leaf Area Index, Specific Leaf Area, and Crown Closure................................ 105 5.2.2 Species and Composition....................................................................................... 106 5.2.3 Biomass..................................................................................................................... 107 5.2.4 Pigments: Chlorophylls, Carotenoids, and Anthocyanins............................... 107 5.2.5 Nutrients: Nitrogen, Phosphorous, and Potassium........................................... 108 5.2.6 Leaf or Canopy Water Content............................................................................. 108 5.2.7 Other Biochemicals: Lignin, Cellulose, and Protein.......................................... 109 5.3 Analysis Techniques and Methods.................................................................................. 109 5.3.1 Derivative Analysis................................................................................................ 109 5.3.2 Spectral Matching................................................................................................... 110 5.3.3 Spectral Index Analysis......................................................................................... 111 5.3.4 Analysis of Absorption Features and Spectral Position Variables.................. 118 5.3.5 Hyperspectral Transformation............................................................................. 120 5.3.6 Spectral Unmixing Analysis................................................................................. 122 5.3.7 Hyperspectral Image Classification..................................................................... 124 5.3.8 Empirical/Statistical Analysis Methods.............................................................. 126 5.3.9 Physically Based Modeling................................................................................... 127 5.4 Summary and Future Directions..................................................................................... 129 Acknowledgments....................................................................................................................... 130 References...................................................................................................................................... 130

5.1  Introduction Imaging spectroscopy, as a new remote-sensing technique (i.e., “hyperspectral remote sensing”), is of growing interest to Earth remote sensing. Hyperspectral remote sensing refers to a special type of imaging technology that collects image data in many narrow contiguous spectral bands (8. A more sensitive indicator of vegetation amount than SAVI at canopy level.

Characteristics and Functions

Delalieux et al. 2008 Qi et al. 1994

Chen 1996; Haboudane et al. 2004 Haboudane et al. 2004 Haboudane et al. 2004 Rouse et al. 1973

Rondeaux et al. 1996

Blackburn 1998

R1250/R1050 0.5[2R800 + 1 − ((2R800 + 1)2 − 8 (R800 − R670))1/2] (R800/R670 − 1)/(R800/R670 + 1)1/2 1.2[1.2(R800 − R550) − 2.5(R670 − R550)] {1.5[1.2(R800 − R550) − 2.5(R670 − R550)]}/{(2R800 + 1)2 − [6R800 − 5(R670)1/2] − 0.5}1/2 (RNIR − RR)/(RNIR + RR)

1.16(R800 − R670)/(R800 + R670 + 0.16)

(R800 − R470)/(R800 + R470)

Zarco-Tejada, Berjon et al. 2005

Jiang et al. 2008

2.5(RNIR − Rred)/(RNIR + 2.4Rred + 1) R554/R677

Huete et al. 2002

Baret and Guyot 1991


a(R800 − aR670 − b)/[(aR800 + R670 − ab + X(1 + a2)], where X = 0.08, a = 1.22, and b = 0.03 2.5(RNIR − Rred)/(RNIR + 6Rred − 7.5Rblue + 1)


Summary of 66 Spectral Indices Extracted from Hyperspectral Data, Collected from the Literature

Table 5.2

112 Advances in Environmental Remote Sensing


Estimate Chls content in anthocyanin-free leaves if NIR is set.

Pigments (Chls, Cars, and Anths) ARI Estimate Anths content from reflectance changes in the green region at leaf level. BGI Estimate Chls and Cars content at leaf and canopy levels. BRI Estimate Chls and Cars content at leaf and canopy levels. CARI Quantify Chls concentration at leaf level.

VARI for red edge ref. (VARIred-edge) WDRVI

SR Visible atmospherically resistant index for green ref. (VARIgreen)


Estimate LAI, vegetation cover, biomass; better than NDVI.

Sensitive to LAI variation at canopy level with a saturation point >8. Estimate LAI and canopy Chls. Similar to OSAVI, but very sensitive to Chls content variations and very resistant to the variations of LAI and solar zenith angle. Same as NDVI. Estimate green vegetation fraction (VF) with minimally sensitive to atmospheric effects with an error of 10° were classified as banks. The stream banks can be considered part of the riparian zone even without the presence of vegetation (Figure 10.4c). Objects enclosed by potential riparian vegetation and stream banks were classified as gaps and included as potential riparian vegetation (Figure 10.4d and e). After merging potential riparian vegetation, banks, and gaps, those objects that did not border the streambed were omitted (Figure 10.4e and f). Elevation differences between the streambed and the external perimeter of the riparian zone provided very useful information for mapping the riparian zone extent to ensure the riparian zones do









200 m


Steep bank slopes

Riparian vegetation





Figure 10.4 (See color insert following page 426.) Object-based image analysis steps for mapping the extent of the riparian zone from light detection and ranging data ((a) through (h)).

Object-Based Image Analysis for Vegetation Mapping and Monitoring


not extend into nonriparian areas in hilly landscapes. Based on field observations, a DTM value of 5 m above the streambed was set as the maximum elevation for riparian zones within a distance of 100 m from the streambed (Figure 10.4g). Potential riparian zone objects were then merged and omitted if they were not in contact with the streambed. As the external riparian zone perimeter is often defined based on the vegetation and canopy extent (Naiman and Decamps 1997), a region-growing algorithm was applied to grow the extent of the riparian zone if PPC >70%, the distance to the streambed was 26 m had detection rates over 90%. Problems were encountered in stands with complex structures, where several individual tree crowns were counted as one tree. The opposite situation, that is, identification of more crowns than the number of trees present, occurred for some deciduous trees and trees with distinct within-canopy foliage clumping (double crowns), where two or more local maxima per tree were detected. The latter case can be considered a general methodological problem utilizing local-maximum-based algorithms. In coniferous stands with an average height >18 m, comparison with on-ground forest inventories revealed results that are suitable for use in operational mapping environments without postclassification corrections. In mixed stands, results depended on the proportion of different species, type of species, and vertical structure of the stands.  Tree Height Derivation The extracted heights of the trees showed a higher accuracy than measurements for class­ ical forest inventories. Comparisons between automated and manual height estimations


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High (b)

subset (c) Low

0 0.5 1 km N

High 0



100 m


Figure 10.9 (See color insert following page 426.) Results of individual tree crown extraction and delineation for the whole study area (right) and subsets showing (a) the normalized digital surface model, (b) the overlaid tree crowns, and (c) the tree crowns with the extracted local maxima (color coded according to the extracted height values).

Table 10.1 Sample-Based Validation of Individual Tree Crown Extraction Average Stand Height (m) 5 years) selective loggings were found during the field trip, and some selective loggings were mapped successfully by the VCT.  Visual Assessment Because the spectral change signals of most forest disturbances can be identified reliably by experienced image analysts (Huang et al. 2008; Masek et al. 2008), especially when images acquired immediately before and after the occurrence of those disturbances are available (Cohen et al. 1998), visual inspection of the disturbances mapped by VCT against the input Landsat images can provide an immediate and still reliable way to evaluate those disturbances (Figure 14.9). Based on this observation, the VCT disturbance year maps were

Forest Change Analysis Using Time-Series Landsat Observations

Predisturbance image

Postdisturbance image








Disturbance year map

Legend for the disturbance year map Persisting nonforest Persisting forest Persisting water Preobservation 1986 1996 1988 1998 1990 2000 1991 2002 1993 2003 1994 2005 Figure 14.9 (See color insert following page 426.) Visual validation of three mapped disturbances using pre- and postdisturbance Landsat images. The disturbance year map was selected from a 17.1 × 11.4 km area in the Uwharrie national forest located in North Carolina (WRS path 16/row 36). The size of each Landsat image chip shown to the left is 2.85 × 2.85 km. (From Huang, C. et al. Remote Sens Environ, 113, 7, 2009. With permission.)

evaluated qualitatively and quantitatively. Qualitative assessments included visual inspection of most of the maps generated by the VCT. When suspicious changes were noticed, the pre- and postdisturbance Landsat images were inspected to determine whether those changes were real. These qualitative assessments revealed that most of the disturbance maps were quite reasonable. Here, a “reasonable” disturbance map was defined as follows: the map had minimum speckles; for human disturbance events such as harvest and logging, the mapped disturbance polygons had regular shapes or linear features that were often the results of human activities and for natural disturbances such as fire and storm, the disturbance patches typically had irregular shapes.  Design-Based Accuracy Assessment To obtain quantitative estimates of the accuracies of the disturbance year maps produced by the VCT, a design-based accuracy assessment was conducted over six sites selected


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Table 14.3 Overall Accuracy, Kappa, and Average Producers’ and Users’ Accuracy Values of the VCT Disturbance Year Products Assessed for All Land Cover and Disturbance Year Classes Seen in Those Products Average Accuracy of Individual Classes WRS Path/ Row 12/31 15/34 21/37 27/27 37/34 45/29

All Classes

Overall Accuracy


0.85 0.80 0.78 0.77 0.86 0.84

0.76 0.75 0.74 0.67 0.43 0.73

Producers’ 0.67 0.67 0.64 0.64 0.31 0.57

Users’ 0.67 0.78 0.79 0.80 0.55 0.72

Disturbance Classes Producers’ 0.63 0.62 0.59 0.60 0.24 0.52

Users’ 0.63 0.77 0.79 0.79 0.52 0.70

to represent different forest biomes and disturbance regimes in the United States. For each site, reference samples were selected with known inclusion probabilities, and those probabilities were considered in deriving accuracy estimates. Such assessments would allow unbiased inference on the accuracy of a map (Stehman 2000). Table 14.3 summarizes the accuracy estimates derived from these assessments. It shows that the disturbance year maps had overall accuracies ranging from 0.77 to 0.86. Except for the southern Utah site (WRS path 37/row 34), the kappa values ranged from 0.67 to 0.76. The producer’s and user’s accuracies averaged over all classes ranged from 0.57 to 0.67 and 0.67 to 0.80, respectively, and ranged from 0.52 to 0.63 and 0.63 to 0.79, respectively, when averaged over the disturbance classes. The average accuracies of the disturbance classes indicate that although those classes were typically rare (up to 1–3% of total area per disturbance year) as compared with no-change classes (Masek et al. 2008; Lunetta et al. 2004), on average, the VCT was able to detect more than half the disturbances with relatively low levels (i.e., 21–37% for five validation sites) of false alarm. 14.4.2  Forest Disturbance in Mississippi and Alabama Mississippi and Alabama are located next to each other in the deep south of the United States, having a total land area of 125,443 km2 and 135,775 km2, respectively. Eighteen WRS path/row tiles are required to cover these two states. Both states are heavily forested, and forestry is a vital component of their economy. Forest management activities and hurricanes and storms along the Gulf coast are the major drivers of forest change. To quantify the rate of forest change in the two states, an LTSS consisting of approximately one image every 2 years from 1984 to 2007 was assembled for each of 18 WRS path/row tiles; the images were then analyzed using the VCT to produce disturbance products and to calculate forest fragmentation metrics (Li et al. 2009a, b). A wall-to-wall disturbance map showing the most recent disturbances was produced for each state by mosaicking the maps at the WRS path/row tile level (Figure 14.10). The results revealed that the two states had widespread disturbances in some coastal areas in recent years, most of which were likely the result of Hurricane Katrina and other tropical storms. Most of the disturbances mapped in inland areas were stand-clearing harvest. The two states had roughly the same level of disturbance rates with similar trends (Figure 14.11). The average annual




120 Kilometers

Water Pre-1986 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Persisting nonforest Persisting forest

Land cover and forest disturbance year



120 Kilometers


Landsat-derived forest disturbances Alabama, 1984–2007

0 15 30

Figure 14.10 (See color insert following page 426.) Disturbance year map derived using the LTSS-VCT approach for Mississippi (left) and Alabama (right).

Landsat-derived forest disturbances Mississippi, 1984–2007

0 15 30

Water Pre-1985 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

County boundary Persisting nonforest Persisting forest

Land cover and forest disturbance year

Forest Change Analysis Using Time-Series Landsat Observations 359


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Percent forest disturbed (%)

15 12 9 6 3 0


1990–94 1995–99 Year interval

2000–04 MS


Figure 14.11 5-year cumulative disturbance rates (percent forest area disturbed) derived using the LTSS-VCT approach in Mississippi and Alabama.

disturbance rate was 1.98% or 1592 km2 for Mississippi, each year from 1985 to 2004, and for Alabama, 2.02% or 1970 km2 for the same period. 14.4.3  Dynamics of National Forests in the Eastern United States The NFs in the United States are managed for multiple purposes, including outdoor recreation, rangeland, timber, watershed, and wildlife and fish (USDA 2007). They are subject to disturbances arising from various management activities and natural events such as fire, storms, insects, and diseases. Continuous monitoring of forest changes arising from such disturbances is essential for assessing the conditions of the NFs and the effectiveness of management approaches. The sample areas selected through the NAFD project (Goward et al. 2008; Huang, Goward, Masek, et al. 2009) covered or intersected with seven NFs in the eastern United States, including the De Soto National Forest in Mississippi, Talladega National Forest in Alabama, Francis Marion National Forest in South Carolina, Uwharrie  National Forest in North Carolina, Chequamegon National  Forest in Wisconsin, Hiawatha National Forest in Michigan, and the Superior National Forest in Minnesota (Figure 14.8). The disturbance maps produced by NAFD project using biennial LTSS allowed an assessment of these NFs and their surrounding areas (Huang, Goward, Schleeweis, et al. 2009). Specifically, the results showed that each of the seven NFs consisted of 90% or more forest land. During the observing period of 1984–2006, about 30–45% of the land pixels in four NFs in the southeastern United States and 10–20% in three NFs in the northern United States were disturbed at least once. For each NF, three buffer zones, defined at 0–5 km, 5–10 km, and 10–15 km from the boundary of the NF, generally had lower percentages of forest land than that within the NF, and the proportion of disturbed forest in the buffer zones were considerably higher than that within the NF. Temporally, the annual disturbance rates varied considerably both within the boundary and in the three buffer zones of each NF. Except for the Uwharrie National Forest, where no obvious trend was found as to whether the NF experienced higher or lower disturbance rates than its buffer zones, the disturbance

Forest Change Analysis Using Time-Series Landsat Observations


rates within the other NFs were generally lower than in their buffer zones during most of the years of the observing period of each LTSS.

14.5  Summary and Conclusions The Landsat record provides a unique data source for understanding the dynamics of land cover and the related surface properties for the decades, dating back to the early 1970s. This chapter presents an approach for reconstructing forest disturbance history over the last few decades using the Landsat record. In this approach, LTSS consisting of a dense time series of IRU quality Landsat observations are produced using streamlined algorithms and procedures, and forest changes are mapped with known disturbance year using the VCT algorithm. This approach has been used to produce disturbance products for many areas in the United States. Two applications of this approach in Mississippi and Alabama and in seven NFs in the eastern United States were summarized in this chapter. Visual assessments of the disturbance year products derived using this LTSS-VCT approach revealed that most of them were reasonably reliable. Design-based accuracy assessment revealed that overall accuracies of around 80% were achieved for disturbances mapped at individual disturbance year level. Average user’s and producer’s accuracies of the disturbance classes were around 70% and 60% for five of the six validation sites, respectively, suggesting that although forest disturbances were typically rare as compared with no-change classes, on average the VCT was able to detect more than half of those disturbances with relatively low levels of false alarms. Field assessment revealed that VCT was able to detect most stand-clearing disturbance events, including harvest, fire, and urban development, while some ­non-stand-clearing events such as thinning and selective logging were also mapped in the western United States. In addition to the disturbance year products for characterizing the occurrence of disturbances, using spectral indices the VCT algorithm also calculates several change magnitude measures and tracks postdisturbance processes. Validation or calibration of these measures and indices requires a time series of ground measurements or other types of reference data sets that match the LTSS acquisitions temporally. Existing reference data sets will not likely be adequate for this purpose, although their availability has yet to be better understood. Obtaining a time series of reference data sets suitable for calibrating or validating data products derived using dense satellite observations should be one of the major goals in planning future reference data collection efforts. The ability to reconstruct forest disturbance history using the LTSS-VCT approach for a given area depends on the availability of a long-term satellite data record consisting of quality, temporally frequent acquisitions for that area. Based on knowledge gained through the NAFD project and an in-depth analysis of the USGS Landsat archive (Goward et al. 2006), most areas in the United States have some Landsat images for use with the LTSS-VCT. An inventory of Landsat holdings at international ground-receiving stations will be needed in order to determine the feasibility of assembling an LTSS at a specific temporal interval for regions outside the United States. To ensure that long-term records of global forest disturbance history can be reconstructed in the future, it is necessary to develop the satellite capabilities today that will allow acquisition of adequate Landsat or Landsat-class images for making at least one cloud-free composite during the peak growing season of every year.


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Acknowledgments Development of the LTSS-VCT approach was made possible through funding support from NASA’s Terrestrial Ecology, Carbon Cycle Science, and Applied Sciences programs, the U.S. Geological Survey, and the LANDFIRE project, which was sponsored by the intergovernmental Wildland Fire Leadership Council of the United States. The NAFD study contributes to the North American Carbon Program. The author wishes to thank Drs. Samuel Goward, Zhiliang Zhu, Jeffrey Masek, and his other collaborators at the University of Maryland, the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center, the NASA Goddard Space Flight Center, and the U.S. Forest Service for their support of the studies described in this chapter.

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Landsat Project Science Office. 2000. Landsat 7 Science Data User’s Handbook. National Aeronautics and Space Administration. toc.html (accessed on March 31, 2008). Law, B. E., S. Van Tuyl, W. D. Ritts, W. B. Cohen, D. Turner, J. Campbell, and O. J. Sun. 2004. Disturbance and climate effects on carbon stocks and fluxes across western Oregon, USA. Glob Chang Biol 10(9):1429–44. Li, M., C. Huang, Z. Zhu, H. Shi, H. Lu, and S. Peng. 2009a. Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model. For Ecol Manage 257(6):1480–8. Li, M., C. Huang, Z. Zhu, H. Shi, H. Lu, and S. Peng. 2009b Use of remote sensing coupled with a vegetation change tracker model to assess rates of forest change and fragmentation in Mississippi, USA. Int J Remote Sens 30(24):6559–74. Lo, C. P., and X. Yang. 2002. Drivers of land-use/land-cover changes and dynamic modeling for the Atlanta, Georgia metropolitan area. Photogramm Eng Remote Sens 68(10):1073–82. Lu, D., P. Mausel, E. Brondízio, and E. Moran. 2004. Change detection techniques. Int J Remote Sens 25(12):2365–407. Lunetta, R. S., D. M. Johnson, J. G. Lyon, and J. Crotwell. 2004. Impacts of imagery temporal frequency on land-cover change detection monitoring. Remote Sens Environ 89(4):444–54. Markham, B. L., and J. L. Barker. 1986. Landsat MSS and TM postcalibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures. EOSAT Landsat Tech Note 1:3–8. Markham, B. L., J. C. Storey, D. L. Williams, and J. R. Irons. 2004. Landsat sensor performance: History and current status. IEEE Trans Geosci Remote Sens 42(12):2691–4. Masek, J. G., C. Huang, W. Cohen, J. Kutler, F. Hall, and R. E. Wolfe. 2008. Mapping North American forest disturbance from a decadal Landsat record: Methodology and initial results. Remote Sens Environ 112:2914–26. Masek, J. G., E. F. Vermote, N. E. Saleous, R. Wolfe, F. G. Hall, K. F. Huemmrich, F. Gao, J. Kutler, and L. Teng-Kui. 2006. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci Remote Sens Lett 3(1):68–72. Peterken, G. F. 2001. Structural dynamics of forest stands and natural processes. In The Forest Handbook, ed. J. Evans. Malden, MA: Blackwell Science. Sahin, V., and M. J. Hall. 1996. The effects of afforestation and deforestation on water yields. J Hydrol 178:293–309. Schwartz, M. D., B. C. Reed, and M. A. White. 2002. Assessing satellite-derived start-of-season measures in the conterminous USA. Int J Climat 22(14):1793–805. Singh, A. 1989. Digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003. Smithson, P., K. Addison, and K. Atkinson. 2008. Fundamentals of the Physical Environment, 4th ed. London: Routledge. Stehman, S. V. 2000. Practical implications of design-based sampling inference for thematic map accuracy assessment. Remote Sens Environ 72:35–45. Teillet, P. M., D. L. Helder, T. A. Ruggles, R. Landry, F. J. Ahern, N. J. Higgs, J. A. Barsi, et al. 2004. A definitive calibration record for the Landsat-5 thematic mapper anchored to the Landsat-7 radiometric scale. Can J Remote Sens 30(4):631–43. Thomas, N., C. Huang, S. N. Goward, S. Powell, K. Rishmawi, K. Schleeweis, and H. Adrienne. Forthcoming. Assessment of NAFD forest change products derived from Landsat Time Series Stacks (LTSS). Remote Sens Environ. Toutin, T. 2003. Geometric correction of remotely sensed images. In Methods and Applications for Remote Sensing of Forests: Concepts and Case Studies, ed. M. Wulder and S. Franklin. Kluwer Academic Publishers, Boston, MA. Townshend, J. R. G., and C. O. Justice. 1988. Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations. Int J Remote Sens 9:187–236. USDA. 2007. USDA Forest Service Strategic Plan FY 2007–2012. United States Department of Agriculture Forest Service, Washington, DC.

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Vermote, E. F., N. Z. El Saleous, and C. O. Justice. 2002. Atmospheric correction of MODIS data in the visible to middle infrared: First results. Remote Sens Environ 83(1–2):97–111. Vermote, E. F., D. Tanre, J. L. Deuze, M. Herman, and J. J. Morcrette. 1997. Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Trans Geosci Remote Sens 35:675–86. Vicente-Serrano, S. M., F. Pérez-Cabello, and T. Lasanta. 2008. Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images. Remote Sens Environ 112(10):3916–34. Waring, R. H., and S. W. Running. 1998. Forest Ecosystems: Analysis at Multiple Scales, 2nd ed. New York: Academic Press. Zartman, C. E. 2003. Habitat fragmentation impacts on epiphyllous bryophyte communities in central Amazonia. Ecology 84(4):948–54. Zhang, X., A. H. Strahler, J. C. F. Hodges, F. Gao, B. C. Reed, A. Huete, M. A. Friedl, and C. B. Schaaf. 2003. Monitoring vegetation phenology using MODIS. Remote Sens Environ 84(3):471–75.

15 Satellite-Based Modeling of Gross Primary Production of Terrestrial Ecosystems Xiangming Xiao, Huimin Yan, Joshua Kalfas, and Qingyuan Zhang Contents 15.1 Introduction......................................................................................................................... 367 15.2 Leaf Area Index, APARcanopy, and FPARcanopy......................................................... 369 15.2.1 Global Production Efficiency Model.................................................................... 370 15.2.2 MODIS Daily Photosynthesis Model................................................................... 371 15.3 Chlorophyll, Light Absorption by Chlorophyll, and FPARchl...................................... 371 15.4 Detailed Description of the Vegetation Photosynthesis Model................................... 372 15.4.1 Model Input Data.................................................................................................... 372 Satellite Data............................................................................................. 372 Climate Data............................................................................................. 373 15.4.2 Estimation of Vegetation Photosynthesis Model Parameters............................ 373 Light Absorption by Chlorophyll.......................................................... 373 Effect of Temperature on Gross Primary Production......................... 374 Effect of Water on Gross Primary Production..................................... 374 Effect of Leaf Age and Phenology on Gross Primary Production................................................................................. 374 Maximum LUE......................................................................................... 375 15.4.3  Model Evaluation.................................................................................................... 376 15.5 Case Study Estimating Gross Primary Production of C4 Maize Cropland Using the Vegetation Photosynthesis Model.................................................................. 376 15.6 Summary.............................................................................................................................. 378 Acknowledgments....................................................................................................................... 379 References...................................................................................................................................... 379

15.1  Introduction Plant photosynthesis occurs within the chloroplasts of plant leaves and is composed of two distinct processes: (1) light absorption, that is, chlorophyll absorbs photosynthetically active radiation (PAR, mostly visible spectrum) from sunlight; and (2) carbon fixation, that is, the absorbed energy is then used to combine water and CO2 to produce sugar (Figure 15.1). Plant photosynthesis is well understood at the chloroplast and leaf levels through direct measurements by instruments (Taiz and Zeiger 2002). However, there is no direct instrument-based measurement of plant photosynthesis at



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H2 O


[ADP + Pi], NADP+

Light Chlorophyll

ATP + NADPH O2 Light reactions

CH2O ∼ P

CO2 + H2O Carbon reactions

Figure 15.1 Schematic diagram of plant photosynthesis that illustrates the light absorption process and the carbon ­fixation process.

the canopy and landscape scales, and how to scale up photosynthesis from individual leaves to the canopy and landscape is still a ­c hallenging and hotly debated topic. At the canopy and landscape scales, photosynthesis is often termed gross primary production (GPP). Application of the eddy covariance technique to measure the net ecosystem exchange (NEE) of CO2 between terrestrial ecosystems and the atmosphere dates back to 1974 (Shaw et al. 1974). In 1990, the first year-long continuous CO2 flux measurements using the eddy covariance technique were conducted at the Harvard forest site in Massachusetts (Wofsy et al. 1993). CO2 flux tower sites provide integrated CO2 flux measurements over footprints with sizes and shapes (linear dimensions typically ranging from hundreds of meters to several kilometers) that vary with the tower height, canopy physical characteristics, and wind velocity (Baldocchi et al. 1996). Continuous measurements of the CO2 NEE between terrestrial ecosystems and the atmosphere through the eddy covariance technique have allowed for more detailed study of ecosystem respiration and GPP at ecosystem and landscape scales (Wofsy et al. 1993). NEE between the terrestrial ecosystem and the atmosphere, as measured at a half-hourly frequency throughout a year, is the difference between the GPP and the ecosystem r­ espiration (Re):

NEE = GPP − Re


Since the early 1990s, more than 600 eddy flux tower sites have been established, covering all major biome types in the world. It is important to note that the footprint sizes of CO2 eddy flux towers are comparable with the spatial resolution of several major satellite observation platforms (e.g., Moderate Resolution Imaging Spectroradiometer [MODIS], SPOT-4/Vegetation). Therefore, several studies have compared the dynamics of satellite-derived vegetation indices with CO2 fluxes from flux towers, with a goal to establish a linkage between the ecosystem metabolism (CO2 flux) and satellite-based observations of vegetation dynamics (Xiao et al. 2004). Due to the changes in climate, soils, land use, and management, however, there are still great uncertainties in estimating seasonal dynamics and spatial variation of GPP at the canopy and landscape scales.

Satellite-Based Modeling of Gross Primary Production of Terrestrial Ecosystems


Satellite-based optical remote sensing platforms (e.g., Landsat, Advanced Very High Resolution Radiometer [AVHRR], SPOT-4/Vegetation, and MODIS) provide frequent observations of the land surface of the entire Earth, and the radiometric values recorded by the optical sensors are associated with the biophysical and biochemical properties of vegetation and soils. As the mathematical transformations are calculated using different spectral bands (e.g., red and near-infrared [NIR]), vegetation indices have been widely used to track vegetation dynamics at the land surface. For example, the normalized difference vegetation index (NDVI), which is calculated from the red and NIR bands of the National Oceanic and Atmospheric Administration (NOAA) AVHRR sensors, is now the longest time-series data record for vegetation study (Myneni et al. 1997; White et al. 2005). NDVI is calculated as follows:


ρNIR − ρred ρNIR + ρred


In the early 1970s, a satellite-based production efficiency model was first proposed to estimate the net primary production (NPP) using photosynthetically active radiation absorbed (APAR) by the vegetation canopy (APARcanopy) and the radiation use efficiency (Monteith 1972, 1977). Since then, a number of models driven by satellite images have been developed to estimate the GPP and NPP (Potter et al. 1993; Field et al. 1995; Prince and Goward 1995; Running et al. 1994; Running et al. 2004; Xiao et al. 2004; Sims et al. 2008; Sims et al. 2006). Satellite-based models of GPP were largely founded on the concept of light-use efficiency (LUE). Depending upon their approaches to estimating APAR for photosynthesis, these production efficiency models (PEMs) can be grouped into two categories based on how they calculate light absorption for photosynthesis: (1) those using the fraction of photosynthetically active radiation (FPAR) absorbed by vegetation canopy (FPARcanopy); and (2) those using the FPAR absorbed by chlorophyll (FPARchl). This chapter aims to provide a brief review of satellite-based PEMs and to highlight the major differences between these two approaches (FPARcanopy and FPARchl). The following discussion is composed of the following: (1) the concepts of leaf area index (LAI), FPARcanopy, and APARcanopy and a brief introduction of two PEMs built upon the concept of FPARcanopy, (2) the concept of chlorophyll, FPARchl, and APARchl, (3) a detailed description of the vegetation photosynthesis model (VPM) that is built upon the concept of FPARchl, and (4) a case study of VPM simulation results from a cropland site.

15.2  Leaf Area Index, APARcanopy, and FPARcanopy LAI, APARcanopy, and FPARcanopy have all been a focus of both the ecology and the remote sensing communities over the past few decades. A number of remote sensing studies have been conducted to develop quantitative relationships between the NDVI and LAI, and between the NDVI and FPARcanopy (Prince and Goward 1995; Ruimy et al. 1999). Approaches based on the NDVI-LAI and NDVI-FPARcanopy relationships have been the dominant paradigm at the crossroads of the fields of remote sensing science and ecology, for example, satellite-based PEMs (Figure 15.2).


GPP = εg × FPARcanopy × PAR FPARcanopy = a × (1 – e-k × LAI) FPARcanopy = f (NDVI) NDVI = f (LAI)

Leaf and canopy water content

LAI-centered algorithms


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Chlorophyll-centered algorithms GPP = εg × FPARchl × PAR Canopy = Chl + NPV FPARcanopy = FPARChl + FPARNPV FPARchl = f (EVI) EVI = 2.5 ×



/(ρ N






LA –ρ x( de IR n N ρ i a =( are VI af ND Le red


ρNIR–ρred ρNIR + 6 × ρred – 7.5 × ρblue + 1


EVI f ch lor leaf ophyll an and can d NPV opy wit h

in a

Figure 15.2 A simple comparison between two paradigms of production efficiency models. GPP = gross primary production; NDVI = normalized difference vegetation index; FPAR = fraction of photosynthetically active radiation; PAR = photosynthetically active radiation; EVI = enhanced vegetation index; LSWI = land surface water index; Chl = chlorophyll; NPV = nonphotosynthetic vegetation; NIR = near-infrared; and SWIR = shortwave infrared.

A number of satellite-based PEMs use the concept of FPARcanopy to estimate the GPP and NPP (Potter et al. 1993; Field et al. 1995; Prince and Goward 1995; Running et al. 2004). GPP is calculated as follows:

GPP = ε g × FPAR canopy × PAR


where εg is the LUE for photosynthesis or GPP. Brief descriptions of two models are provided in Sections 15.2.1 and 15.2.2. 15.2.1  Global Production Efficiency Model The global production efficiency model (GLO-PEM) estimates both the GPP and NPP based on the production efficiency approach (see Equation 15.3). It has several linked components that describe the processes of canopy radiation absorption, utilization, autotrophic respiration, and the regulation of these processes by environmental factors (Prince and Goward 1995; Goetz et al. 2000). The GLO-PEM uses NDVI to estimate FPARcanopy (see Goward and Huemmrich 1992 for more details):

FPAR canopy = 1�08 × NDVI − 0�08


Satellite-Based Modeling of Gross Primary Production of Terrestrial Ecosystems


In the GLO-PEM, εg is estimated through a modeling approach based on plant physiological principles (Prince and Goward 1995). Plant photosynthesis depends on both the capacity of the photosynthetic enzymes to assimilate CO2 (Collatz et al. 1991; Farquhar et al. 1980) and the stomatal conductance of CO2 from the atmosphere into the intercellular spaces (Harley et al. 1992). These two processes are affected by environmental factors, such as air temperature, water vapor pressure deficit, soil moisture, and atmospheric CO2 concentration. Detailed descriptions of approaches for modeling εg have been provided in many earlier publications (Prince and Goward 1995; Collatz et al. 1991; Goetz and Prince 1998; Collatz et al. 1992; Goetz and Prince 1999). 15.2.2  MODIS Daily Photosynthesis Model The photosynthesis (PSN) model uses Equation 15.3 to estimate GPP, but εg and FPARcanopy are derived using different methods (Running et al. 2004; Running et al. 1999; Running et al. 2000). FPARcanopy is produced as a part of the MOD15 (LAI and FPAR) product suite. In MOD17, a set of biome-specific maximum LUE parameters is extracted from the biome properties lookup table (Running et al. 2000).

ε g = ε 0 × Tscalar × Wscalar


where ε0 is the maximum LUE, Tscalar is estimated as a function of daily minimum temperature, and Wscalar is estimated as a function of daylight average water vapor pressure deficit. In this approach, biome is defined according to the MODIS land-cover product (MOD12) (Running et al. 2004; Running et al. 2000; Friedl et al. 2002).

15.3  Chlorophyll, Light Absorption by Chlorophyll, and FPARchl From the biochemical perspective, vegetation canopies are composed of chlorophyll (chl) and nonphotosynthetic vegetation (NPV). The latter includes both canopy-level (e.g., stem, senescent leaves) and leaf-level (e.g., cell walls, vein, and other pigments) materials. Therefore, FPARcanopy should be partitioned into FPARchl and FPAR absorbed by NPV (FPARNPV) (Xiao et al. 2004a,b; Xiao et al. 2005a).

Canopy = chlorophyll + NPV


FPAR canopy = FPAR chl + FPAR NPV


How much difference is there between FPARcanopy and FPARchl in a vegetation canopy? Does the difference between FPARcanopy and FPARchl change over the plant growing season? Using a radiative transfer model (PROSAIL2) and daily MODIS data, results from temperate deciduous forests (Zhang et al. 2005; Zhang et al. 2006) have shown that FPARcanopy is significantly larger than FPARchl, and the difference between FPARcanopy and FPARchl changes as much as 30%–40% over the plant growing season (Figure 15.3).


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FPARcanopy, FPARleaf, or FPARchl


FPARcanopy in 2001 FPARcanopy in 2002


FPARcanopy in 2003 FPARleaf in 2001


FPARleaf in 2002 FPARleaf in 2003


FPARchl in 2001 FPARchl in 2002


FPARchl in 2003

0.5 0.4 140


180 200 Day of year (DOY)



Figure 15.3 A comparison of the fractions of photosynthetically active radiation absorbed by canopy, leaf, and chlorophyll (FPARcanopy, FPAR leaf, and FPARchl respectively), as illustrated in a deciduous broadleaf forest at the Harvard ­forest site, Massachusetts (see Zhang et al. 2005 for more details).

As shown in Figure 15.1, photosynthesis starts with light absorption by leaf chlorophyll. Only the PAR absorbed by chlorophyll (product of PAR × FPARchl) is responsible for photosynthesis or GPP. Based on the conceptual partitioning of chlorophyll and NPV within a leaf and canopy, the VPM was developed for estimating GPP over the photosynthetically active period of vegetation (Xiao et al. 2004a). The VPM is briefly described as follows:

GPP = ε g × FPAR chl × PAR


This biochemical approach, based on the chlorophyll–FPARchl relationship, is currently an emerging paradigm in the field of remote-sensing–based PEMs, and other additional models have been developed using the FPARchl concept (Sims et al. 2008; Sims et al. 2006; Mahadevan et al. 2008). Figure 15.2 summarizes the major differences between FPARcanopy and FPARchl approaches in estimating light absorption and GPP.

15.4  Detailed Description of the Vegetation Photosynthesis Model 15.4.1  Model Input Data  Satellite Data The satellite-based VPM uses two vegetation indices as input data: the enhanced vegetation index (EVI) and the land surface water index (LSWI). These vegetation indices differ from the widely used NDVI (Equation 15.2). NDVI is often applied in PEMs to estimate the

Satellite-Based Modeling of Gross Primary Production of Terrestrial Ecosystems


vegetation productivity of terrestrial ecosystems (Field et al. 1995; Prince and Goward 1995; Nemani et al. 2003). It is known that NDVI suffers several limitations, including sensitivity to atmospheric conditions, sensitivity to soil background (e.g., soil moisture), and saturation of index values in multilayered and closed canopies (Xiao et al. 2004). EVI directly adjusts the reflectance in the red band as a function of the reflectance in the blue band, accounting for residual atmospheric contamination (e.g., aerosols), variable soil, and canopy background reflectance (Huete et al. 1997):


G(ρNIR − ρred ) ρNIR + (C1ρred − C2 ρblue) + L


where G is 2.5, C1 is 6, C2 is 7.5, and L is 1, and ρNIR, ρred, and ρblue are land surface reflectances of the NIR, red, and blue bands, respectively. Because the shortwave infrared (SWIR) spectral band is sensitive to vegetation water content and soil moisture, a combination of NIR and SWIR bands has been used to derive water-sensitive vegetation indices (Xiao et al. 2004b; Ceccato et al. 2002a,b; Ceccato et al. 2001). LSWI is calculated as the normalized difference between NIR and SWIR spectral bands (Xiao et al. 2002):




where ρNIR and ρSWIR are reflectances of the NIR and the SWIR band, respectively. Satellite images from two advanced optical sensors (vegetation onboard SPOT-4 satellite and MODIS onboard Terra satellite) have blue, red, NIR, and SWIR bands, which allow the calculation of EVI and LSWI indices. EVI and LSWI have now been used widely to char­ acterize the growing conditions of vegetation (Zhang et al. 2003; Boles et al. 2004).  Climate Data The climate input data sets for the VPM include daily minimum temperature (°C), daily maximum temperature (°C), and the daily sum of PAR (mol/day). The daily climate data come from either in situ measurements (e.g., CO2 flux tower sites, weather stations) or climate model simulations (e.g., NCEP Reanalysis climate data), depending upon the availability of climate data (Zhang et al. 2009; Zhao et al. 2005; Raich et al. 1991). 15.4.2 Estimation of Vegetation Photosynthesis Model Parameters  Light Absorption by Chlorophyll In the VPM, FPARchl within the photosynthetically active period of vegetation is estimated as a linear function of EVI, and the coefficient, a, is set to be 1.0 (Xiao et al. 2004a,b):

FPAR chl = α × EVI



Advances in Environmental Remote Sensing  Effect of Temperature on Gross Primary Production Temperature affects photosynthesis; there are a number of ways to estimate the effect of  temperature on photosynthesis (Tscalar). In the VPM, Tscalar is estimated at each time step using the equation developed for the terrestrial ecosystem model (Raich et al. 1991):

Tscalar =

(T − Tmin )(T − Tmax ) [(T − Tmin )(T − Tmax )] − (T − Topt )2


where Tmin, Tmax, and Topt are the minimum, maximum, and optimum temperatures for photosynthetic activities, respectively. If air temperature falls below Tmin, Tscalar is set to be zero. The values of the Tmin, Tmax, and Topt parameters vary with vegetation types.  Effect of Water on Gross Primary Production Wscalar, the effect of water on plant photosynthesis, has been estimated as a function of soil moisture and/or water vapor pressure deficit in a number of PEMs (Field et al. 1995; Prince and Goward 1995; Running et al. 2000). For instance, in the Carnegie Ames Stanford Approach (CASA) model, soil moisture was estimated using a one-layer bucket model (Malmstrom et al. 1997). Soil moisture represents water supply to the leaves and canopy, and water vapor pressure deficit represents evaporative demand in the atmosphere. The leaf and canopy water content is largely determined by dynamic changes of both the soil moisture and water vapor pressure deficit. The availability of time-series data of NIR and SWIR bands from the new generation of advanced optical sensors (e.g., variable geometry turbocharger, MODIS) offers opportunities for quantifying the canopy water content at large spatial scales through both the vegetation indices approach (Ceccato et al. 2002a) and the radiative transfer modeling approach (Zarco-Tejada et al. 2003). Vegetation indices that are based on NIR and SWIR bands are sensitive to changes in equivalent water thickness (g/cm2) at the leaf and canopy levels (Ceccato et al. 2002a,b; Ceccato et al. 2001; Hunt and Rock 1989). As a first-order approximation, the VPM uses a satellite-derived water index to estimate the seasonal dynamics of Wscalar:

Wscalar =

1 + LSWI 1 + LSWI max


where LSWImax is the maximum LSWI value within the plant growing season for individual pixels.  Effect of Leaf Age and Phenology on Gross Primary Production The leaf age affects the seasonal patterns of photosynthetic capacity and NEE in deciduous forest (Wilson et al. 2001). Turner et al. (Turner et al. 2003) compared daily LUE from four CO2 flux tower sites: an agriculture field, a tall grass prairie, a deciduous broadleaf forest, and a boreal forest. Their results suggested that parameters on cloudiness and the

Satellite-Based Modeling of Gross Primary Production of Terrestrial Ecosystems


phenological status of vegetation should be included in modeling vegetation primary production. In the VPM, Pscalar is used to account for the effect of the leaf age on photosynthesis at the canopy level. Calculation of Pscalar is dependent upon leaf longevity (deciduous versus evergreen). For a canopy that is dominated by leaves with a life expectancy of 1 year (one growing season, e.g., deciduous trees and shrubs), Pscalar is calculated at two different phases:

Pscalar =

1 + LSWI (From bud-burst to complete leaf expansion) 2

Pscalar = 1 (After complete leaf expansion)

(15.14) (15.15)

Evergreen trees and shrubs have a green canopy throughout the year, because foliage is retained for several years. The canopy of evergreen forests is thus composed of green leaves of various ages. To deal with different age classes in evergreen forest canopies, fixed turnover rates of foliage of evergreen forests at the canopy level have been used in some process-based ecosystem models (Aber and Federer 1992; Law et al. 2000). For evergreen forests, we simply assume Pscalar  = 1 (Xiao et al. 2004b); we also assume this for tundra, grassland, and cropland (e.g., wheat) vegetation, which have new leaves emerging through most of the plant growing season (Li et al. 2007). Maximum LUE LUE (εg) is affected by temperature, water, and leaf phenology:

ε g = ε 0 × Tscalar × Wscalar × Pscalar


where ε0 is the apparent quantum yield or the maximum LUE (μmol CO2/μmol PPFD), and Tscalar, Wscalar, and Pscalar are the scalars for the effects of temperature, water, and leaf phenology on the LUE of vegetation, respectively. A full description of the VPM is given elsewhere (Xiao et al. 2004b; Xiao et al. 2005a). The maximum LUE (ε0) for individual vegetation types can be estimated from the nonlinear analysis of the observed half-hourly NEE and incident PAR data from eddy covariance flux tower sites. In the VPM, the ecosystem-level ε0 values vary with vegetation types. The Michaelis–Menten function (Equation 15.17) is used to estimate the ε0 values of individual vegetation types; half-hourly NEE and PAR data for weekly to 10-day periods within the peak period of the plant growing season (e.g., from July to August) are used:


α × PPFD × GPPmax − Re α × PPFD + GPPmax


where α is the maximum LUE or apparent quantum yield (as photosynthetic photon flux density [PPFD] approaches 0), GPPmax is the maximum gross ecosystem exchange, and Re


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is the ecosystem respiration. The estimated α value is used as an estimate of the ε0 parameter in the VPM. 15.4.3  Model Evaluation Evaluating GPP estimates at the canopy level is a challenging task. Recent progress in partitioning the observed NEE data into GPP and Re makes it possible to directly evaluate GPP estimates from various models. Daily GPP and Re flux data at individual flux sites are generated from half-hourly NEE flux data by the CO2 eddy covariance flux community (Baldocchi et al. 2001; Mizoguchi et al. 2009).

15.5 Case Study Estimating Gross Primary Production of C4 Maize Cropland Using the Vegetation Photosynthesis Model The VPM has been evaluated for and applied to several major biome types, including tropical rainforests (Xiao et al. 2005b), temperate deciduous broadleaf forests (Xiao et al. 2004a; Wu et al. 2009), evergreen needle-leaf forests (Xiao et al. 2004b; Xiao et al. 2005a), alpine tundra (Li et al. 2007), grassland (Wu et al. 2008), and winter wheat–maize croplands (Yan et al. 2009). Here, we present a case study of C4 maize cropland to illustrate the model simulation. The Rosemount G21 site is located at the University of Minnesota’s Rosemount Research and Outreach Center, approximately 25 km south of St. Paul, MN (Baker and Griffis 2005). The site has silty loam soil with a surface layer of high organic carbon content. It has a temperate continental climate, and the plant growing season begins in May and ends in October. In 2005, maize was planted at this site, and no irrigation occurred during the entire growing season. In this case study, MODIS 8-day Land Surface Reflectance (MOD09A1) data sets were downloaded from the EROS Data Center of the U.S. Geological Survey (http:// The MODIS sensor onboard the National Aeronautics and Space Administration (NASA) Terra satellite has 36 spectral bands. Seven spectral bands are primarily designed for the study of vegetation and land surface: blue (459–479 nm), green (545–565 nm), red (620–670 nm), NIR (841–875 nm, 1230–1250 nm), and SWIR (1628–1652 nm, 2105–2155 nm). The reflectance values of four spectral bands (blue, red, NIR [841–875 nm], and SWIR [1628–1652 nm]) during 2005 were used to calculate vegetation indices (NDVI, EVI, and LSWI). Time series of vegetation indices for one MODIS pixel, within which an eddy covariance flux tower is located, were used for the VPM simulation. In this case, the extent of the flux tower’s footprint (60% vegetated dark surfaces. Kaufman and Tanré (1998) and Kaufman and Sendra (1987) give equations for estimating the surface reflectances for red and blue wavelengths from their correlations with a SWIR band representing surface reflectances (Equations 17.2 and 17.3):

ρ0�47 µm = 0�25 ⋅ ρ2⋅12 µm


ρ0�66µm = 0�5 ⋅ ρ2⋅12 µm


It is assumed that due to aerosol there is difference between the original TOA reflectances from the blue and red bands and the surface reflectances derived from the SWIR band. This difference is then fitted to a best-fit aerosol model, with the knowledge of the expected aerosol types in the study area, for example, continental (Lenoble and Brogniez 1984), industrial or urban (Remer et al. 1996), biomass burning (Hao and Liu 1994), and marine (Husar, Prospero, and Stowe 1997), to obtain an AOT value for each image waveband. Dubovik et al. (1998) suggested that a window with a size of 10 km gives the best ­signal-to-noise ratio for global aerosol retrieval using MODIS. However, this method has several limitations, including (1) only coarse resolution that is suitable for global monitoring can be achieved, (2) its operation is limited to vegetated areas and cannot operate over bright urban surfaces, and (3) it has low accuracy in southeast China (Kaufman and Tanré 1998). In addition, Chu et al. (2002) showed that collection-4 algorithm (DDV’s algorithm, Equations 17.2 and 17.3) had a positive bias in comparison to the AERONET sun photometer data. Remer et al. (2005) and Levy et al. (2004) also reported certain inherent problems in determining surface reflectance using the MODIS collection-4 algorithm. Their results imply that ­i naccurate surface properties can lead to errors (±0.05 ± 0.2τ) in aerosol retrieval.


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Due to these perceived errors, Levy et al. (2007) then modified the algorithm by considering band correlation based on the normalized difference vegetation index NDVISWIR, and the scattering angle, since Gatebe et al. (2001) and Remer, Wald, and Kaufman (2001) suggested the VISIBLE/SWIR ratio is angle dependent. The rationale of Levy et al.’s collection-5 method is to first identify the dark pixels. A kernel of 10-km size is used for scanning, and the dark pixels are identified as those with surface reflectance less than 0.25 at 2.12 μm wavelength. The darkest 20% and the brightest 50% of pixels inside the box are discarded, and the remaining 30%, or at least 12 pixels, inside the box are used for NDVISWIR calculation (Equation 17.4). Following this, the pixels are classified into three categories based on the NDVISWIR (Equation 17.5), and the f linear equations (Equation 17.6) are applied to those three categories with three sets of slope and intercepts. The values of linear equations are determined by band correlation analysis using atmospherically corrected MODIS images.


(ρ1�24µm − ρ2�12 µm ) (ρ1�24µm + ρ2�12 µm )


NDVI SWIR < 0�25 0�25 < NDVI SWIR < 0�75 NDVI SWIR > 0�75


ρ0�66µm = f1 (ρ2�12 µm ) ρ0�47 µm = f2 (ρ2�12 µm )


The collection-5 algorithm also operates over bright surfaces if the surface reflectance at 2.12 μm is less than 0.4 and the number of pixels inside the 10-km kernel is greater than 12. Then, a 0.47-μm channel is used for aerosol retrieval, and the continental model is assigned during the lookup table (LUT) calculation. This capability for bright surface aerosol retrieval combined with the increased threshold of surface reflectance for dark pixel selection (0.15 in DDV and 0.25 in collection-5) allows the collection-5 algorithm to work over semiurban and suburban areas, although the method still does not work well over large and very bright surfaces, such as deserts or complex land surfaces. Also, since only one band at the 0.47 μm wavelength is used and only one aerosol model is assigned for aerosol retrieval, the quality of AOT over bright surfaces is deemed poor, with greater uncertainties. Nevertheless, when the new collection-5 algorithm was evaluated (Li et al. 2007; Mi et al. 2007), a significant improvement was found, with an increase of 27% in accuracy over the original DDV algorithm, when correlated with ground measurements. Aerosol retrieval over bright surfaces is challenging because the land surface and atmospheric aerosol contents are not easy to differentiate because both have high reflectance values. The operational DDV and collection-5 algorithms retrieve aerosols over land when the surface reflectances are less than 0.15 and 0.25 at a 2.12 μm wavelength, respectively. They are unable to retrieve aerosols over large bright surface areas like the Mongolian and Saharan deserts, which are the most important sources of dust in China and Africa. Hsu et al. (2004, 2006) recently developed the deep blue algorithm for aerosol retrieval over bright surfaces such as desert, arid, and semiarid areas using MODIS images. This algorithm makes use of ratio between two blue wavelengths (412 and 490 nm) since the surfaces are bright in the red region and darker in the blue region. The deep blue algorithm has been demonstrated successfully only for large homogeneous surfaces such as deserts and not for areas of complex land cover, like urban areas.


Remote Sensing of Aerosols from Space

17.3 High-Resolution Aerosol Observations of Densely Urbanized Region (Case Study of Hong Kong and the Pearl River Delta Region) 17.3.1  Study Area Hong Kong (Figure 17.1), a city with a service-based economy located in southeast China, has suffered serious air pollution over the last decade. The Hong Kong PolyU AERONET station shows aerosol levels to be high, compared with other urban stations worldwide, for example a mean AOT of 0.69 for 440-nm band, compared with 0.57 for Beijing, 0.55 for Singapore, 0.22 for Rome, and 0.24 for the Goddard Space Flight Center. The Pearl River Delta (PRD) region is often covered with haze and gray smoke, which is observed on daily MODIS satellite images. Wu et al. (2005) showed that the AOT in this region is often higher than 0.6 at 550 nm. Previous studies in the PRD region have measured a range of particle concentrations for PM10 (particulate matter with aerodynamic diameter less than or equal to 10 μm) of 70–234 μg/m3, with high average PM10 concentrations of above 200 μg/m3 in winter, and around 100 μg/m3 for PM2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) in autumn (Wei et al. 1999; Cao et al. 2003, 2004). These high concentrations of suspended particulates create low visibility and greatly affect the regional radiative budget (Wu et al. 2005). During the long dry season in winter, northeasterly air masses mainly bring continental pollution into the PRD region and Hong Kong (Gnauk et al. 2008). The consequent effects on visibility and health due to continuous bad air have appeared gradually since 2000. The Hong Kong Environmental Protection Department (EPD 2004) reported that an increase of 10 μg/m3 in the concentration of NOx, SO2, respiratory suspended particulate (RSP), and ozone causes associated diseases such as respiratory, chronic pulmonary, and cardiovascular heart diseases to increase by 0.2–3.9%, respectively. Ko et al. (2007) demonstrated that air pollution is accompanied by increased hospital admissions for chronic obstructive pulmonary disease in Hong Kong, especially during winter. Wong et al. (1999) also found significant relationships between hospital admissions in Hong Kong for all respiratory diseases, all cardiovascular diseases, chronic obstructive pulmonary diseases, and heart failure, and concentrations of the following 114.20

Guangzhou 10 m

Tap Mun

Yuen Long

Huizhou 3 m

Panyu 1 m

Tsuen Wan

Dongguan 7 m 22.30

Zhuhai 2 m

Pearl River Estuary

Macau 1 m

Shenzhen 10 m


Tung Chung



Shunde 1 m

Zhongshan 4 m



Foshan 7 m




Hong Kong 7 m 114.00


AOT_550 nm_MODO4_C005

25 kms 0.0





114.40 Air quality monitoring stations 2.0

Figure 17.1 Left: Hong Kong and cities of the Pearl River Delta region, with population size (millions). Right: Study area of Hong Kong overlaid with five air quality monitoring stations, two AERONET stations, and a MODIS collection-5 aerosol optical thickness image on January 28, 2007.


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four pollutants: nitrogen dioxide, sulfur dioxide, ozone, and PM10. The low visibility due to air pollutants in Hong Kong also affects marine and air navigation and affects the ­attractiveness of Hong Kong as a tourist destination. The gathering of data over large regions such as the Hong Kong and Guangdong provinces of China (area of approximately 179,000 km2) is a major challenge to air pollution monitoring. The 16 air monitoring stations set up over this region are obviously insufficient for detailed observation. It is now realized that the only way to obtain measurements of aerosols over uninstrumented areas is from space (NASA 2009), and that this is the only way by which long-term global monitoring of aerosols can be done. Images showing synoptic coverage over the PRD region as well as large parts of China often covered with gray haze are now available from the MODIS satellite sensor of the National Aeronautic and Space Administration (NASA), which provides images of the globe on a twice-daily basis, at spatial resolutions of 250 m, 500 m, and 1 km. From these, NASA has developed the MODIS level-2 AOT product (MOD04). However, the 10-km spatial resolution of this product only provides meaningful depictions on a regional scale, and aerosol monitoring over complex regions, such as urban and rural areas in Hong Kong territories (1095 km2), requires more spatial detail. 17.3.2  Methodology  Contrast Reduction Method and Li et al. Method The heterogeneous land algorithm, also known as the contrast reduction method (Tanré et al. 1988), is based on the principle of measuring the blurring effect between highly contrasting adjacent pixels (Tanré et al. 1979; Mekler and Kaufman 1980). This has been used by Sifakis and Deschamps (1992), Sifakis, Soulakellis, and Paronis (1998), and Retalis, Cartalis, and Athanassiou (1999) for very “high”-resolution aerosol estimation over complex urban areas such as Athens using SPOT and Landsat images. A fairly high correlation of 0.76 was obtained between Landsat-derived AOT and SO2 over Athens (Sifakis, Soulakellis, and Paronis 1998). The major drawback of this method for deriving high-resolution aerosol images is that it measures path radiance (aerosol scattering between adjacent image pixels) within a kernel of 15 × 15 pixels. Thus, for SPOT, a 20-m pixel produces an aerosol image of 300 × 300 m. Using MODIS, the resolution of the resulting aerosol product is 7.5 × 7.5 km. This resolution is too low for the spatial detail required over densely urbanized regions. In addition, accuracy is said to be sensitive to the selected aerosol model because particle shape and size distribution are crucial to the specular scattering and reflectance properties. Li et al. (2005) developed a 1-km AOT algorithm for Hong Kong, using the same principles as that of the DDV algorithm of Kaufman and Tanré (1998), but under more stringent conditions for the cloud mask and vegetation screening. Error was within 15–20% compared with handheld sun photometer measurements, although too few validation sources were available in the region to obtain robust AOT measurements from the satellite data. Furthermore, the DDV algorithm used would not give accurate results over bright urban areas. Li et al. (2005) further suggested that based on 44 measurements from MICROTOPSII sun photometers, the sulfate and biomass burning models found in the second simulation of a satellite signal in the solar spectrum (6S) radiative transfer model were unsuitable for Hong Kong. Indeed, the diversity of aerosol sources in the region coupled with often high humidity poses challenges for finding a suitable model. Because the MICROTOPSII measurements used for validation in the study lack inversion data such as size distribution and


Remote Sensing of Aerosols from Space

SSA, which are available from AERONET, more rigorous studies are needed to provide an operational aerosol retrieval method for complex regions.  Our Method In order to estimate aerosols over variable cover types, including bright and dark surfaces, a newly developed methodology is described here for aerosol retrieval from MODIS 500-m data (Wong et al. 2009, 2010). For this study, five MODIS 500-m channels and two MODIS 250-m channels were acquired for aerosol retrieval, as well as for cloud and water masking. Figure 17.2 illustrates this AOT retrieval method. The AERONET data (2005–2007) from the Hong Kong PolyU station were acquired and clustered to give four different aerosol models. The aerosol models coupled with relative humidity data and different viewing geometries were input into the Santa Barbara DISORT radiative transfer (SBDART; Ricchiazzi et al. 1998) code to build LUTs. The LUT construction was based on the four local aerosol models, namely, (1) mixed urban aerosol (which is a mixture of urban and marine pollutants), (2) polluted urban aerosol (which is dominated by local urban aerosol), (3) dust (which is long-distance Asian dust), and (4) heavy pollution

AERONET data MODIS L1B 500-m calibrated reflectances

Radiative transfer model - SBDART - Viewing geometry - Aerosol type - Relative humidity

Cloud screening, satellite viewing angles 1°). The MRT obtained the surface reflectance by extracting the minimum reflectance (or darkest) ­pixels for a land surface from many Rayleigh corrected images over a period. For validation purposes, the derived surface reflectance images were compared with field measurements and MODIS surface reflectance products (MOD09). The aerosol reflectances can then be derived by decomposing the TOA reflectances from surface reflectance and Rayleigh path reflectances (Equation 17.7). The derived aerosol reflectances are then compared with simulated aerosol reflectances from LUTs using the spectral fitting technique. The aerosol model with minimum residual is selected, and the corresponding aerosol reflectance and AOT values are obtained. Finally, the AOT images at 550 nm are derived:

ρTOA = ρAer + ρRay +

Γ Tot (θ0 ) ⋅ Γ Tot (θs ) ⋅ ρSurf 1 − ρSurf ⋅ rHem


where θ0 is the solar zenith angle, θs is the satellite zenith angle, ρAer is the aerosol reflectance, ρRay is the Rayleigh reflectance, Γ Tot (θ0 ) is the transmittance along the path from the sun to the ground, Γ Tot (θs ) is the transmittance along the path from the sensor to the ground, ρSurf is the surface reflectance, and rHem is the hemispheric reflectance. When the derived AOT images are compared with a whole year’s measurements with AERONET and MICROTOPSII, a strong correlation is observed between MODIS ­collection-5 AOT and AERONET data (Figure 17.3a). This is surprising because Kaufman and Tanré (1998) predicted AOT retrieval problems for the southern region of China, due to high humidity combined with a diversity of aerosol types. Similar correlations (r2 = 0.79 for AERONET and r2 = 0.76 for MICROTOPSII) are obtained for 500-m AOT data (Figures 17.3b and c), compared with r2 = 0.83 for the MODIS collection-5. In addition, similar RMS errors (MODIS 500 m = 0.176, collection-5 = 0.167), similar MAD errors (MODIS 500 m = 0.142, ­collection 5 = 0.129), and similar bias estimators (MODIS 500 m = 0.011, collection-5 = 0.011) are also obtained from MODIS 500-m data compared with the MODIS operational ­collection-5 products at 10-km resolution. It is significant that 500-m AOT data not only retrieves AOT images at a much higher spatial resolution but also retrieves AOT over bright urban surfaces as well as dark vegetated areas. Both of these improvements are important for a topographically complex area with heterogeneous land cover like Hong Kong.


Remote Sensing of Aerosols from Space



C004: y = 0.765x + 0.046


r 2 = 0.769, SD = 0.165 C005: y = 0.725x + 0.051



AERONET 550 nm

AERONET 550 nm



r 2 = 0.833, SD = 0.102







C005 C004

0.2 0.0

y = 0.736 + 0.25x r 2 = 0.793, SD = 0.104





0.6 0.8 1.0 MOD04 550 nm (a) 1.4



0.2 1.6




y = 0.716x + 0.132 r 2 = 0.760, SD = 0.107, n = 14



0.6 0.8 1.0 MODIS 550 nm (b)





MicrotopsII 550 nm

1.0 0.8 0.6 0.4 0.2 0.0



0.4 0.6 0.8 1.0 MODIS (500 m) 550 nm (c)



Figure 17.3 Scatter plots of (a) MODIS collection-4 and collection-5 aerosol optical thickness (AOT) data versus AERONET AOT measurements, (b) MODIS 500-m AOT data versus AERONET AOT measurements, and (c) MODIS 500-m AOT data versus MICROTOPSII AOT measurements.

Although the signal-to-noise ratio of the 10-km resolution data is 20 times higher than the 500-m resolution data (Kaufman and Tanré 1998), Henderson and Chylek (2005) showed that there are only small changes in the accuracy of aerosol retrieval with increasing pixel sizes from 40 × 80 m2 to 2040 × 4080 m2. Therefore, any loss of accuracy due to a decreased signal-to-noise ratio of 500-m AOT data is believed to be small enough to be compensated by an increased accuracy from higher spatial resolution. The AOT distribution over Hong Kong and the PRD region on October 20, 2007 from MODIS 500-m data is shown in Figure 17.4b. Only approximately 15 pixels cover the entire territories of Hong Kong, with an AOT image at 10-km resolution (Figure 17.4a), while there are 400 times more using 500-m resolution (Figure 17.4b). The spatial pattern of aerosols, especially in Shenzhen (the Chinese city near Hong Kong), is much more precisely defined using the 500-m AOT image compared with the 10-km pixels of MOD04. Figure 17.4c shows the 500-m AOT images over Kowloon peninsula and part of Hong Kong island overlaid


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112.20 112.60 113.00 113.40 113.80 114.20 114.60 23.80









22.20 112.20 112.60 113.00 113.40 113.80 114.20 114.60 AOT_550 nm_MOD04_C005 0.0






(a) 112.20 112.60 113.00 113.40 113.80 114.20 114.60 22.20 22.60 23.00 23.40 23.80

23.80 23.40 23.00 22.60 22.20 112.20 112.60 113.00 113.40 113.80 114.20 114.60 AOT_550 nm_MODIS_500 m 0.0







AOT_550 nm_MODIS_500 m 0.0

0.25 0.50 0.75 1.00 1.25

(c) Figure 17.4 (See color insert following page 426.) Aerosol optical thickness image at 550 nm, (a) derived from MODIS collection-5 algorithm, (b) derived from MODIS 500-m data, and (c) derived from MODIS 500-m data overlaid with road layer.


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with road networks. Urban districts like Hung Hom, Sham Shui Po, Kowloon Bay, and Ap Lei Chau observe high AOT values (∼1.0), whereas the rural areas have relatively low AOT values (∼0.3).  Applications of High-Resolution Aerosol Products  Monitoring Anthropogenic Emissions in the PRD Region An example of rapid changes in AOT over the PRD region occurred on January 28, 2007 and January 30, 2007. Two MODIS 500-m AOT images are shown in Figures 17.5a and b. The AOT at 550 nm on January 28, 2007 is relatively low with a range of ∼0.4 in rural areas to ∼1.4 in urban areas. It is particularly notable that in the industrialized areas of the PRD, for example, in Guangzhou city and Shunde district, high AOT values are observed, but in other areas, low AOT values are observed due to strong wind speeds (∼4 m/s). However, a marked increase in AOT occurred 2 days later on January 30, 2007. This extremely high

112.20 112.60 113.00 113.40 113.80 114.20 114.60 22.20 22.60 23.00 23.40 23.80

23.80 23.40 23.00 22.60 22.20 112.20 112.60 113.00 113.40 113.80 114.20 114.60 20070280315_AOT_500 m 0.0






(a) 112.20 112.60 113.00 113.40 113.80 114.20 114.60 22.20 22.60 23.00 23.40 23.80

23.80 23.40 23.00 22.60 22.20 112.20 112.60 113.00 113.40 113.80 114.20 114.60 20070300300_AOT_500 m 0.0






(b) Figure 17.5 (See color insert following page 426.) Aerosol optical thickness image at 550-nm and 500-m resolution over Hong Kong and the Pearl River Delta region on (a) January 28, 2007 and (b) January 30, 2007.


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AOT (∼1.8), which is observed over most industrialized areas in the PRD, is shown in red in Figure 17.5b. Many industries and power plants are located there, and due to very low wind speeds (∼1 m/s) on that day, pollutants were trapped in the PRD region. The pollutants would progressively accumulate as wind speeds decreased from 4 m/s on January 28, 2007 to 2 m/s on January 29, 2007 and 1 m/s on January 30, 2007.  Mapping Aerosols from Biomass Burning China is still an agricultural country, and had a yield of 690 million tons of straw in 2000 (Wang et al. 2007), of which 36% was used for domestic fuel and 7% was disposed of by open fires (Gao et al. 2002). In the PRD region with extensive areas of dense forest, biomass burning (intentional or accidental) occurs frequently. This section demonstrates the application of MODIS 500-m AOT images to locate and pinpoint local sources of biomass burning. Figure 17.6a shows the Rayleigh-corrected RGB image on November 30, 2007. Biomass burning is clearly evident on the left of the image (marked on the image), which is located 112.20 112.60 113.00 113.40 113.80 114.20 114.60 22.20 22.60 23.00 23.40 23.80

23.80 23.40 23.00 22.60 22.20 112.20 112.60 113.00 113.40 113.80 114.20 114.60 20073340300_AOT_550 m








(b) 112.20 112.60 113.00 113.40 113.80 114.20 114.60

22.60 22.20

22.20 22.60 23.00



23.80 23.40 23.00 112.20 112.60 113.00 113.40 113.80 114.20 114.60 MODIS_10 km_collection5_2007334.0300 0.0






(c) Figure 17.6 (See color insert following page 426.) (a) Rayleigh-corrected RGB image on November 30, 2007, (b) aerosol ­optical thickness (AOT) image at 500-m resolution, and (c) AOT collection-5 image at 10-km resolution.

Remote Sensing of Aerosols from Space


in the dense forest area of Zhaoqing county. MODIS 500-m AOT images (Figure 17.6b) are also observed to have high AOT values (>1.8). The smoke plumes and the source of burning can be easily identified on the 500-m AOT image, whereas they cannot be identified on the MODIS 10-km AOT image. In addition, large patches over urban areas are masked out with no AOT data values on MODIS collection-5 algorithm (Figure 17.6c) due to their high surface reflectances not meeting the surface reflectance criteria in the collection-5 AOT algorithm. The same fire spots are also depicted on the Web Fire Mapper developed by the Geography Department of the University of Maryland. An easterly wind from a wind direction map confirms the direction of fire smoke. The images derived from our method not only can retrieve aerosols over bright urban surfaces but also can pinpoint small pollution sources such as biomass burning.

17.4  Summary Satellite remote sensing for aerosol retrieval has been developed over 3 decades, and the techniques now permit aerosol mapping at global, regional, and local scales. Operational satellite aerosol products are now available from space agencies such as the National Oceanic and Atmospheric Administration (NOAA), NASA, and the European Space Agency (ESA). This chapter reviews different algorithms operating on MODIS images, including the DDV algorithm (known as collection-4 algorithm), the second-generation MODIS operation algorithm (known as collection-5 algorithm), the deep blue algorithm at 10-km resolution, Li et al.’s (2005) method at 1-km resolution, and Wong et al.’s (2009, 2010) method at 500-m resolution. We described Wong et al.’s (2009, 2010) algorithm using the MODIS 500-m resolution images for the retrieval of aerosol properties over complex urbanized regions such as Hong Kong and the PRD region. Strong correlations with AERONET (r2 = 0.79) and MicrotopsII (r2 = 0.76) sun photometer measurements, as well as low RMS error (0.176), low MAD error (0.142), and low bias estimator (0.011) were obtained for MODIS 500-m AOT data. The aerosol retrieval methodology presented here can be transferred to other mega cities. The MODIS 500-m AOT images are able to locate local-scale anthropogenic emissions such as traffic “black spots” and industrial emissions and to map rapid changes in AOT at regional scales. Moreover, aerosols from biomass burning can be identified using the MODIS 500-m AOT images, as the smoke plume and the source of burning can be easily identified. As such, with the high temporal resolution of MODIS, the 500-m AOT images can be used to monitor cross-boundary aerosols and the development of pollutant sources in the PRD region surrounding Hong Kong. Two major impediments to the use of remote sensing for routine air quality monitoring over complex regions are the need for several images per day for accurate forecasting and the unknown relationship between AOT, which represents the whole atmospheric column, and air pollution levels near the ground. The first impediment may be overcome by the provision of geostationary satellites with multispectral sensors in the visible region or a constellation of satellites to supplement MODIS TERRA and AQUA, such as the forthcoming National Polar-Orbiting Operational Environmental Satellite System (NPOESS) program. The second one may be minimized as AOT retrievals become more accurate with improved future sensors and algorithms. Then, the inclusion of local meteorological data, including wind speed, humidity, and inversion height, should permit the retrieval


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of fractional column AOT concentrations, including the near-ground layers. The establishment of robust relationships between fractional column AOT concentrations and pollutants of concern, such as fine particulates (PM2.5), aerosol precursor gases, ozone, and oxides of nitrogen, in the future will make aerosol remote sensing an essential tool for city and regional environmental authorities.

Acknowledgments The authors wish to thank the NASA Goddard Earth Science Distributed Active Archive Center for the MODIS level-1 and -2 data, Professor Zhanqing Li and Dr. Kwon Ho Lee for their valuable advice, Hong Kong CERG Grant PolyU 5253/07E, and PolyU PDF Research Grant G-YX1W.

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Remer, L. A., Y. J. Kaufman, D. Tanré, S. Mattoo, D. A. Chu, J. V. Martins, R. R. Li et al. 2005. The MODIS aerosol algorithm, products, and validation. J Atmos Sci 62(4):947–73. Remer, L. A., A. E. Wald, and Y. J. Kaufman. 2001. Angular and seasonal variation of spectral surface reflectance ratios: Implications for the remote sensing of aerosol over land. IEEE Trans Geosci Rem Sens 39(2):275–83. Retalis, A., C. Cartalis, and E. Athanassiou. 1997. Assessment of the distribution of aerosols in the area of Athens with the use of Landsat Thematic Mapper data. Int J Rem Sens 20(5):939–45. Ricchiazzi, P., S. R. Yang, C. Gautier, and D. Sowle. 1998. SBDART: A research and teaching software tool for plane-parallel radiative transfer in the Earth’s atmosphere. Bull Am Meteorol Soc 79(10):2101–14. Sifakis, N., and P. Y. Deschamps. 1992. Mapping of air pollution using SPOT satellite data. Photogramm Eng Rem Sens 58:1433–7. Sifakis, N., N. A. Soulakellis, and D. K. Paronis. 1998. Quantitative mapping of air pollution density using earth observations: A new processing method and applications to an urban area. Int J Rem Sens 19:3289–300. Tanré, D., P. Y. Deschamps, C. Devaux, and M. Herman. 1988. Estimation of Saharan aerosol optical thickness from blurring effects in Landsat Thematic Mapper data. J Geophys Res 93:15955–64. Tanré, D., M. Herman, P. Y. Deschamps, and A. de Leffe. 1979. Atmospheric modelling for space measurements of ground reflectances, including bidirectional properties. Appl Optic 18:3587–94. Tanré, D., Y. J. Kaufman, M. Herman, and S. Mattoo. 1997. Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. J Geophys Res 102:16971–88. Vaughan, M., S. Young, D. Winker, K. Powell, A. Omar, Z. Liu, Y. Hu, and C. Hostetler. 2004. Fully automated analysis of space-based lidar data: An overview of the CALIPSO retrieval algorithms and data products. Proc SPIE 5575:16–30. Wang, Q., M. Shao, Y. Liu, K. William, G. Paul, X. Li, Y. Liu, and S. Lu. 2007. Impact of biomass burning on urban air quality estimated organic tracers: Guangzhou and Beijing as cases. Atmos Environ 41:8380–90. Wanner, W., A. H. Strahler, B. Hu, P. Lewis, J. P. Muller, X. Li, C. L. Barker Schaaf, and M. J. Barnsley. 1997. Global retrieval of bidirectional reflectance and albedo over land from EOS MODIS and MISR data: Theory and algorithm. J Geophys Res 102(17):143–62. Wei, F., E. Teng, G. Wu, W. Hu, W. E. Wilson, R. S. Chapman, J. C. Pau, and J. Zhang. 1999. Ambient concentrations and elemental compositions of PM10 and PM2.5 in four Chinese cities. Environ Sci Technol 33(23):4188–93. Wong, T. W., T. S. Lau, T. S. Yu, A. Neller, S. L. Wong, W. Tam, and S. W. Pang. 1999. Air pollution and hospital admissions for respiratory and cardiovascular diseases in Hong Kong. Occup Environ Med 56:679–83. Wong, M. S., J. E. Nichol, K. H. Lee, and Z. Q. Li. 2009. High resolution aerosol optical thickness retrieval over the Pearl River Delta region with improved aerosol modeling. Sci China Earth Sci 52(10):1641–9. Wong, M. S., J. E. Nichol, K. H. Lee, and Z. Q. Li. 2010. Retrieval of aerosol optical thickness using MODIS 500 × 500 m2, a study in Hong Kong and Pearl River Delta region. IEEE Trans Geosci Rem Sens 46(8):3318–27. Wu, D., X. Tie, C. Li, Z. Ying, A. K. H. Lau, J. Huang, X. Deng, and X. Bi. 2005. An extremely low visibility event over the Guangzhou region: A case study. Atmos Environ 39:6568–77.

18 Remote Estimation of Chlorophyll-a Concentration in Inland, Estuarine, and Coastal Waters Anatoly A. Gitelson, Daniela Gurlin, Wesley J. Moses, and Yosef Z. Yacobi Contents 18.1 Introduction......................................................................................................................... 439 18.2 Background..........................................................................................................................440 18.3 Semianalytical NIR–Red Model.......................................................................................443 18.4 Data and Methods..............................................................................................................446 18.4.1 Field Measurements...............................................................................................446 18.4.2 Laboratory Measurements..................................................................................... 447 18.4.3 Descriptive Statistics of Water Quality Parameters...........................................448 18.4.4 Model Calibration and Validation Using Satellite Data....................................448 18.5 Results.................................................................................................................................. 450 18.5.1 Optical Properties of the Constituents................................................................ 450 18.5.2 Spectral Reflectance Properties............................................................................ 451 18.5.3 Calibration and Validation of NIR–Red Models Using Proximal Sensing.......................................................................... 455 18.5.4 Calibration and Validation of NIR–Red Models Using Satellite Data.................................................................................. 458 18.6 Toward a Universal NIR–Red Algorithm........................................................................ 460 18.7 Limitations and Challenges in Developing Satellite Algorithms................................ 462 18.7.1 Atmospheric Correction........................................................................................ 462 18.7.2 Temporal Variation of Water Quality................................................................... 462 18.7.3 Within-Pixel Spatial Heterogeneity...................................................................... 463 18.7.4 Need for a Modified In Situ Data Collection Strategy......................................463 18.8 Conclusions..........................................................................................................................464 References......................................................................................................................................464

18.1  Introduction Inland, estuarine, and coastal waters comprise only a small fraction of the Earth’s aquatic component, but are extensively exploited by human activities. The water quality in these ecosystems is, therefore, of high ecological and economic importance, and in this respect, quantitative evaluation of phytoplankton biomass is a crucial endeavor. Despite the high variability of its composition, size, and forms (Reynolds 2006), phytoplankton may be relatively easily monitored by the estimation of the concentration of chlorophyll-a (chl-a), a pigment universally found in all phytoplankton species and routinely used as a substitute for biomass in all types of aquatic environments. 439


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Remote sensing is an effective method for synoptic monitoring of chl-a concentration over potentially heterogeneous areas of phytoplankton distribution. Even a few remotely sensed images are useful in the design or improvement of in situ sampling strategies by identifying representative locations and optimizing the timing for sampling. Remote sensing studies typically involve mapping of constituent concentrations in water bodies using water-leaving radiance or reflectance collected by a sensor held above or below the water surface. The estimation of constituent concentrations usually requires the development of a model, which is a mathematical combination of reflectances at different wavelengths, in such a way that the model is maximally sensitive to changes in the concentration of the constituent of interest (e.g., chl-a) and is minimally sensitive to changes in concentrations of other constituents present in the water. In this chapter, we will present algorithms for the remote estimation of chl-a concentration in turbid protective waters and show how they work at close range and at satellite altitude. This chapter also contains • Brief background information on the commonly used remote sensing models for estimating chl-a concentration • A description of a semianalytical model that uses reflectances in the red and nearinfrared (NIR) wavelengths for estimating chl-a concentration • The data and methods used • The results of calibrating and validating the NIR–red models using reflectance data measured in situ and from satellites • The results supporting the potential for a universal NIR–red algorithm • A discussion on the challenges and limitations in developing a universal NIR–red algorithm for accurately estimating chl-a concentration from the satellite data routinely acquired over turbid productive waters from around the globe

18.2  Background Historically, remote sensing of chl-a concentration has been commonly used for open ocean, case I waters (Morel and Prieur 1977) using reflectances in the blue and green spectral regions (Gordon and Morel 1983; Kirk 1994; Mobley 1994). In turbid productive waters, however, the reflectances in these spectral regions cannot be used for estimating chl-a concentration due to overlapping and uncorrelated absorption by colored dissolved organic matter (CDOM) as well as scattering and absorption by detritus and tripton, which are higher in turbid waters than in open oceans (Figure 18.1; GKSS 1986; Gitelson 1992; Dekker 1993; Gons 1999; Gons et al. 2000; Dall’Olmo and Gitelson 2005; Schalles 2006; Gitelson, Schalles, and Hladik 2007). Many investigations have been directed toward the development of remote sensing techniques for the estimation of the concentration of chl-a and other water constituents in turbid productive waters (Bukata et al. 1979; Vasilkov and Kopelevich 1982; Gitelson, Keydan, and Shishkin 1985; Vos, Donze, and Bueteveld 1986; Gitelson, Kondratyev, and Garbusov 1987; Gitelson and Kondratyev 1991; Gitelson 1992; Dekker 1993; Gitelson et al. 1993a; Gitelson, Szilagyi, and Mittenzwey 1993b; Jupp, Kirk, and Harris 1994; Bukata et al. 1995; Gege 1995; Gons 1999; Gons et al. 2000; Pierson and Strömbäck 2000; Kallio et al. 2001; Kutser et al. 2001;


Remote Estimation of Chlorophyll-a Concentration






a (λ) (m−1)



2.5 2.0 1.5 1.0 0.5 0.0 400



550 600 Wavelength (nm)




Figure 18.1 Spectra of the absorption coefficients of phytoplankton, aφ(λ), nonalgal particles, aNAP(λ), CDOM, aCDOM(λ), and water, aw(λ) for a moderately turbid lake with a chl-a concentration of 27.8 mg ⋅ m−3 and total suspended ­solids (TSS) concentration of 6.5 mg ⋅ L−1. (Values of aw(λ) taken from Mueller, J. L. 2003. Inherent optical properties: Instrument characterizations, field measurements and data analysis protocols. In Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume IV, Erratum 1 to Pegau, S., J. R. V. Zaneveld, and J. L. Mueller. 2003. Inherent optical property measurement concepts: Physical principles and instruments. In Inherent Optical Properties: Instruments, Characterizations, Field Measurements, and Data Analysis Protocols. Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume IV, ed. J. L. Mueller, G. S. Fargion, and C. R. McClain. Goddard Space Flight Center Technical Memorandum 2003-211621.)

Strömbäck and Pierson 2001; Ruddick et al. 2001; Thiemann and Kaufmann 2002; Kallio, Koponen, and Pulliainen 2003; Albert 2004). The two main approaches used in the construction of algorithms for the remote estimation of chl-a concentration in turbid productive waters were analytical and empirical/semianalytical. The analytical approach is based on specific inherent optical properties (IOP), such as absorption and scattering coefficients per unit concentration of constituents, which are used to simulate reflectance spectra by using a radiative transfer technique. Then, by a process of optimization based on minimizing the difference between the simulated and measured reflectance spectra, the concentrations of different constituents are adjusted and subsequently determined. If the values of the specific IOPs included in the model are fairly close to those of the water body where the reflectance data were measured, this approach may yield accurate results. However, specific IOPs vary widely in space and time even within a water body. Thus, the assumption of a priori IOP determination is not often valid for turbid productive waters. The empirical or semianalytical approach involves algorithms that are based on relationships between physically based models and experimental results. The spectral features of turbid productive waters have been studied for a wide range of chl-a concentrations from 3 to more than 200 mg ⋅ m−3 (Gitelson, Keydan, and Shishkin 1985; Gitelson et al. 1986; Gitelson, Kondratyev, and Garbusov 1987; Gitelson and Kondratyev 1991; Gitelson 1992; Quibell 1992; Dekker 1993; Gitelson 1993; Gitelson et al. 1993a; Gitelson, Szilagyi, and Mittenzwey 1993b; Gitelson et al. 1994; Han et al. 1994; Han and Rundquist 1994; Matthews and Boxall 1994; Millie et al. 1995; Rundquist, Schalles, and Peake 1995; Yacobi, Gitelson, and Mayo 1995; Han and Rundquist 1996; Vos, Donze, and Bueteveld 1986; Han and Rundquist 1997; Gons 1999; Gitelson et al. 2000; Gons et al. 2000; Dall’Olmo, Gitelson, and Rundquist 2003; Dall’Olmo and Gitelson 2005; Dall’Olmo 2006; Dall’Olmo and Gitelson 2006; Schalles 2006; Gitelson, Schalles, and Hladik 2007; Gitelson et al. 2008;


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Gitelson et al. 2009; Moses 2009; Moses et al. 2009a,b). Three main features of chl-a are potentially important in the context of concentration estimation using spectral reflectance. First, chl-a has a strong absorption band around 670 nm, forming a trough in the reflectance spectrum. The magnitude of reflectance around 670 nm (ρ670) is related to chl-a concentration. However, chl-a absorption is often not the sole factor controlling ρ670, as it depends also on the concentration of inorganic and organic suspended solids (ISS and OSS). Thus, ρ670 alone cannot be used for a reliable estimation of chl-a concentration (Dekker 1993; Gitelson et al. 1993a; Gitelson, Szilagyi, and Mittenzwey 1993b; Gitelson et al. 1994; Yacobi, Gitelson, and Mayo 1995). A peak due to solar-induced chl-a fluorescence near 685 nm is the second significant spectral feature in the red region (Neville and Gower 1977; Gower 1980; Doerffer 1981). With an increase in chl-a concentration, the fluorescence peak near 685 nm increases; therefore, it was used as an indicator of chl-a concentration, which was calculated as the height above the baseline positioned from 650 through 730 nm (Neville and Gower 1977; Gower 1980; Doerffer 1981; GKSS 1986; Fischer and Kronfeld 1990). However, the quantitative accuracy of this approach is limited by the varying fluorescence efficiencies of different phytoplankton populations and changes in water absorption, which reduce the available light. Another limitation is the reabsorption of the fluoresced light by chl-a, resulting in a decrease in the emitted signal; this happens when the chl-a concentration increases above 10–15 mg ⋅ m−3 (Kishino, Sugihara, and Okami 1986; Gitelson 1992; Gitelson 1993). Thus, although the use of chl-a fluorescence at 685 nm seems to be useful and effective for the estimation of low chl-a concentrations, it is not expedient for the development of algorithms that yield consistent and accurate results for a wide range of chl-a concentrations in turbid productive waters with highly variable optical properties. The third reflectance feature specific to chl-a is a peak in the NIR region around 700 nm. The magnitude of the peak, as well as its position, depends on the chl-a concentration (Vasilkov and Kopelevich 1982; Gitelson et al. 1986; Vos, Donze, and Bueteveld 1986; Gitelson 1992; Gitelson 1993; Gitelson et al. 1994; Yacobi, Gitelson, and Mayo 1995, Schalles et al. 1998) but is also affected by absorption and scattering by other constituents. Most of the algorithms developed to quantify chl-a concentration are based on the properties of the peak near 700 nm. These include the ratio of the reflectance peak (ρmax) to ρ670 (ρmax/ρ670), the ratio ρ705/ρ670 (Gitelson et al. 1986; Gitelson and Kondratyev 1991; Dekker 1993; Gitelson et al. 1993a; Gitelson, Szilagyi, and Mittenzwey 1993b), and the position of this peak (Gitelson 1992). Gons (1999) used the ratio of reflectances at 704 and 672 nm, the absorption coefficients of water at these wavelengths, and the backscattering coefficient at 776 nm to estimate chl-a concentrations ranging from 3 to 185 mg ⋅ m−3. In many studies, close relationships have been found between chl-a concentrations and NIR-to-red reflectance ratios, with the red wavelength around 675 nm and the NIR wavelength varying between 700 and 725 nm (Hoge, Wright, and Swift 1987; Yacobi, Gitelson, and Mayo 1995; Pierson and Strömbäck 2000; Pulliainen et al. 2001; Ruddick et al. 2001; Oki and Yasuoka 2002; Dall’Olmo and Gitelson 2005). Using vector analysis, Stumpf and Tyler (1988) showed that the ratio of reflectances in the NIR and red bands of the Advanced Very High Resolution Radiometer (AVHRR) and Coastal Zone Color Scanner (CZCS) can be used to identify phytoplankton blooms and to estimate chl-a concentrations above 10 mg ⋅ m−3 in turbid estuaries. These methods are based on the assumption that optical parameters such as the ­specific absorption coefficient of phytoplankton, a*ϕ(λ), and the chl-a fluorescence quantum yield, η, remain constant. In reality, these parameters depend on the physiological state and structure of the phytoplankton community and can vary widely. It was shown that a*ϕ(675) can vary up to fourfold for chl-a concentrations ranging from 0.02 to 25 mg ⋅ m3 (Bricaud

Remote Estimation of Chlorophyll-a Concentration


et al. 1995). Fluorescence quantum yield is affected by the taxonomic composition of phytoplankton, illumination conditions, light adaptation, nutritional status, and temperature and can vary by eightfold for chl-a concentrations ranging from 2 to 30 mg ⋅ m−3, which is typical for inland and coastal waters (GKSS 1986). Therefore, the assumptions of constant a*ϕ(λ) and η are a significant source of uncertainty in models for the remote estimation of chl-a concentrations.

18.3  Semianalytical NIR–Red Model A fundamental relationship between the remote sensing reflectance (ρrs) and IOPs was formulated as follows (Gordon, Brown, and Jacobs 1975):

ρrs (λ) ∝

bb ( λ ) a(λ) + bb (λ)


where a(λ) is the absorption coefficient and bb(λ) is the backscattering coefficient. Recently, a conceptual model based on Equation 18.1 was developed and used to estimate pigment concentration in terrestrial vegetation at leaf and canopy levels (Gitelson, Gritz, and Merzlyak 2003; Gitelson et al. 2005):

Pigment content ∝ [ρ−1 (λ 1 ) − ρ−1 (λ 2 )] × ρ(λ 3 )


where ρ(λ1), ρ(λ2), and ρ(λ3) are the reflectance values at wavelengths λ1, λ2, and λ3 respectively. λ1 is in a spectral region such that ρ(λ1) is maximally sensitive to absorption by the pigment of interest, although it is still affected by absorption by other pigments and scattering by all particulates. λ2 is in a spectral region such that ρ(λ2) is minimally sensitive to absorption by the pigment of interest and its sensitivity to absorption by other constituents is comparable to that of ρ(λ1). Thus, the difference [ρ−1(λ1) − ρ−1(λ2)] is related to the concentration of the pigment of interest. However, the difference is still potentially affected by variations in scattering by particles. Consequently, information on λ3 is required. Wavelength λ3 is located in a spectral region where the reflectance ρ(λ3) is minimally affected by absorption due to any constituent and is therefore used to account for the variability in scattering between samples. Dall’Olmo, Gitelson, and Rundquist (2003) suggested the use of this conceptual model (Equation 18.2) for estimating chl-a concentration in turbid productive waters. In Equation 18.1, the absorption coefficient, a(λ), is the sum of the absorption coefficients of water, aw(λ), phytoplankton, aϕ(λ), nonalgal particles, aNAP(λ), and CDOM, aCDOM(λ). Follow­ ing Gordon’s concept, the model presented in Equation 18.2 (called henceforth the threeband NIR–red model) was designed by choosing three optimal wavelengths, such that the contributions due to absorption by constituents other than chl-a and backscattering by particles are kept to a negligible minimum, and the model output is maximally sensitive to chl-a concentration. The red region around 670 nm, where chl-a absorption is maximal (but the reflectance may be affected also by other constituents), was chosen as λ1. λ2 is longer than λ1, where absorption by chl-a, aϕ(λ), is minimal and the absorption by other constituents, aNAP(λ) and aCDOM(λ), is about the same as at λ1. Thus, ρ−1(λ1) is a measure of absorption by chl-a and other constituents, and ρ−1(λ2) is a measure of absorption by constituents other than chl-a. λ3 is at a wavelength beyond λ2 in the NIR region, where the absorption


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by all particles and dissolved constituents is null. The backscattering coefficient is considered spectrally uniform across the range of wavelengths considered (from λ1 through λ3; Dall’Olmo and Gitelson 2005), which is a fundamental assumption in the model. The subtraction of ρ−1(λ2) from ρ−1(λ1) isolates the absorption by chl-a as follows: ρ−1 (λ 1 ) − ρ−1 (λ 2 ) ∝

aw λ1 + aφλ1 + aNAP λ1 + aCDOM λ1 + bb (λ) bb ( λ ) ρ−1 (λ 1 ) − ρ−1 (λ 2 ) ∝

aw λ 2 + aφλ 2 + aNAP λ 2 + aCDOM λ 2 + bb (λ)

aφ + aw λ1 − aw λ 2 bb ( λ )

bb ( λ )


Another assumption is that the absorption by water at λ3 is much greater than the total backscattering, such that aw(λ3) >>bb(λ) and a(λ) ≅ aw(λ3).

ρ(λ 3 ) ∝

bb ( λ ) aw λ 3


Considering the fact that the absorption by water, aw(λ), is independent of the c­ onstituent concentrations and ignoring its dependence on temperature, the model has the following form:

[ρ−1 (λ 1 ) − ρ−1 (λ 2 )] × ρ(λ 3 ) ∝ aφ (λ)


Absorption by phytoplankton is related to chl-a concentration as follows:

aφ (λ) = aφ* (λ) × Cchl-a


where a*ϕ(λ) is the chl-a specific absorption coefficient and Cchl-a is the concentration of chl-a. Thus, the three-band NIR–red model was finally formulated as

[ρ−1 (λ 1 ) − ρ−1 (λ 2 )] × ρ(λ 3 ) ∝ chl-a


Dall’Olmo and Gitelson (2005) have found that the optimal wavelengths for the accurate estimation of chl-a concentrations in the range of 2 to 180 mg ⋅ m−3 were as follows: λ1 = 670 nm, λ2 = 710 nm, and λ3 = 740 nm. Testing the model for several data sets collected in inland and estuarine waters, Gitelson, Schalles, and Hladik (2007) and Gitelson et al. (2008) found relatively wide optimal spectral bands of wavelengths of λ1 = 660–670 nm, λ2 = 700–720 nm, and λ3 = 730–760 nm, which provided accurate estimations of chl-a concentration with the three-band NIR–red model. For waters that do not have significant concentrations of nonalgal particles and colored dissolved organic matter, the subtraction of ρ−1(λ2) in the model may be omitted (Dall’Olmo and Gitelson 2005), which leads to the special case of a two-band NIR–red model (Stumpf and Tyler 1988):

ρ−1 (λ 1 ) × ρ(λ 3 ) ∝ chl-a

where λ1 is in the red region and λ3 in the NIR region beyond 730 nm.


Remote Estimation of Chlorophyll-a Concentration


Another two-band model, which is different in its formulation from the previously mentioned two-band model (Equation 18.8), is (Gitelson 1992; Gitelson, Szilagyi, and Mittenzwey 1993)

ρ−1 (λ 1 ) × ρ(λ 2 ) ∝ chl-a


where λ1 is in the red region and λ2 is in the region of the reflectance peak, around 700–710 nm. Recently, a four-band model was suggested (Le et al. 2009) for the estimation of chl-a concentration in productive waters with very high concentrations of inorganic suspended matter:

[ρ− 1 (662) – ρ− 1 (693)] × [ρ− 1 (740) – ρ− 1 (7 05)]− 1

The three-band NIR–red model was modified by including one more spectral band, ρ705, in order to reduce the effect of variations in scattering by suspended matter. The Medium Resolution Imaging Spectrometer (MERIS), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) are three commonly used spaceborne optical sensors, whose data may be used for the estimation of chl-a concentration using NIR–red models. The spectral bands in the red and NIR regions for these sensors are as follows: • MERIS—Spectral bands centered at 665 nm (band 7), 681 nm (band 8), 708 nm (band 9), and 753 nm (band 10) • MODIS—Spectral bands centered at 667 nm (band 13), 678 nm (band 14), and 748 nm (band 15) • SeaWiFS—Spectral bands centered at 670 nm (band 6) and 765 nm (band 7) The proximity of the 681-nm MERIS band and the 678-nm MODIS band to the chl-a fluorescence wavelength at 685 nm means that the variable quantum yield of fluorescence (Dall’Olmo and Gitelson 2006) might affect the accuracy of chl-a concentration estimated using these bands. Therefore, these bands were eliminated as candidates for inclusion in NIR–red models for estimating chl-a concentration. Thus, the NIR–red models for estimating chl-a concentration using satellite data are as follows: Three-band MERIS NIR–red model based on Equation 18.7:

chl-a ∝ [(ρband7 )−1 − (ρband9 )−1 ] × (ρband10 ) 


Two-band MERIS NIR–red model based on Equation 18.9:

chl-a ∝ (ρband7 )−1 × (ρband9 )


Two-band MODIS NIR–red model based on Equation 18.8:

chl-a ∝ (ρband13 )−1 × (ρband15 )


An appropriate equivalent for Equation 18.12 for SeaWiFS should be based on bands 6 and 7 of that sensor. Note that the two-band MERIS NIR–red model (Equation 18.11) is


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fundamentally different from the two-band MODIS NIR–red model (Equation 18.12), yielding significantly different results.

18.4  Data and Methods 18.4.1  Field Measurements The field data were collected at 89 stations from July through November 2008 and at 63 stations from April through July 2009 at the Fremont Lakes State Recreation Area in eastern Nebraska, and included a standard set of optical water quality parameters. In the field, water transparency was measured with a standard Secchi disk, and turbidity was measured with a HACH 2100 portable turbidimeter. Surface water samples were collected at a depth of 0.5 m and stored on ice in a dark container. Water samples for wet laboratory ­analysis of chl-a concentrations, total particulate absorption coefficients, ap(λ), absorption coefficients of non-algal particles, aNAP(λ), and absorption coefficients of phytoplankton, aφ(λ), were filtered through 25-mm Whatman GF/F filters within 24 hours after collection. The filters for the extraction of chl-a were stored in a freezer at a temperature of −18°C for a maximum of 4 weeks. Surface water samples for laboratory analysis of TSS, ISS, and OSS were filtered through 47-mm Whatman GF/F filters within 42 hours. The filtrates were filtered through 47-mm Whatman 0.2-μm nylon membranes for laboratory analysis of the absorption coefficients of CDOM, aCDOM (λ). Backscattering coefficients, bb(λ), were measured in the field in 2009 with a customized ECO Triplet sensor. The bb(λ) measurements were corrected for salinity, and absorption by water (Mueller 2003), particulates, and CDOM (absorption coefficients were taken from laboratory measurements). Hyperspectral reflectance measurements were taken by means of two intercalibrated Ocean Optics USB2000 miniature fiber optic spectrometers. Data were collected over optically deep water in the range of 400–900 nm in intervals of ∼0.3 nm with a spectral resolution of ∼1.5 nm. Radiometer 1, equipped with a 25° field-of-view optical fiber, was pointed downward to measure the below-surface upwelling radiance, Lu(λ), at nadir. The tip of the optical fiber was kept just below the water surface by means of a 2-m long, hand-held dark pole on the sunlit side of the boat. To simultaneously measure the incident irradiance Ed(λ), radiometer 2, connected to an optical fiber fitted with a cosine collector, was pointed upward and mounted on a 2.5-m mast. The integration time of radiometer 2 was up to 10 times shorter than the integration time of radiometer 1. Hyperspectral reflectance measurements were collected from 10 a.m. to 2 p.m. The solar zenith angles ranged from a maximum of 66.44° in November 2008 to a minimum of 18.16° in June 2009. The critical issue with regard to the dual-fiber approach is that the transfer functions of the radiometers used for measuring upwelling and downwelling fluxes, which describe the relationship of the incident flux measured by the sensor to the data numbers produced by the radiometers, should be identical. We studied the identity of the two radiometers used in this study and found that the difference between their transfer functions did not exceed 0.4% (Dall’Olmo and Gitelson 2005). To match their transfer functions, the radiometers were intercalibrated by measuring simultaneously the upwelling radiance, Lref(λ), from a white Spectralon® reflectance standard (Labsphere, Inc., North Sutton, NH), and correspondingly, the irradiance

Remote Estimation of Chlorophyll-a Concentration


i­ ncident on the reflectance standard, Eref(λ). The remote sensing reflectance at nadir was ­computed by  L (λ) Eref (λ)  ρ (λ ) t ρrs (λ) =  u × × 100 × ref × 2 × F (λ )  E ( λ ) L ( λ ) π n ref  d 

where Lu(λ) is the below-surface upwelling radiance at nadir, Ed(λ) is the incident irradiance, t is the water-to-air transmittance (taken as equal to 0.98), ρref(λ) is the irradiance reflectance of the Spectralon® reflectance standard linearly interpolated to match the wavelength of each radiometer, π is used to transform the irradiance reflectance into remote sensing reflectance, n is the refractive index of water relative to air (taken as equal to 1.33), and F(λ) is the spectral immersion factor computed after Ohde and Siegel (2003). The reflectance spectra were collected and processed using the CALMIT Data Acquisition Program (CDAP) developed at the Center for Advanced Land Management Information Technologies (CALMIT) at the University of Nebraska—Lincoln. 18.4.2 Laboratory Measurements Pigments were extracted in subdued light conditions in a laboratory at the University of Nebraska—Lincoln. The samples were extracted for 5 minutes in 10 mL of 99.5% ethanol at 78°C and cooled in the dark for 4 hours (modified from Nusch 1980). The samples were then centrifuged for 5 minutes in a Cole-Parmer EW-17250-10 fixed-speed centrifuge, and chl-a concentrations were quantified fluorometrically with a Turner 10-AU-005 CE fluorometer on the same day (Welschmeyer 1994). The instrument was calibrated every 3 months with a 100 μg ⋅ L−1 chl-a standard prepared from C6144-1MG Anacystis nidulans chl-a (Sigma-Aldrich). The Anacystis nidulans chl-a was dissolved in 1 L of 99.5% ethanol, and the concentration was determined spectrophotometrically (Ritchie 2008). Standard curves with a series of 10 dilutions were made at the time of the calibration to study the linearity of the single point calibration of the instrument for chl-a concentrations from 0 to 200 μg ⋅ L−1. Concentrations of TSS, ISS, and OSS were determined gravimetrically (Eaton et al. 2005). Particulate absorption coefficients were measured by the quantitative filter technique (Mitchell et al. 2003). The suspended particles were concentrated on 25-mm Whatman GF/F filters, and spectral measurements of the optical density (OD) were made within 1.5 hours after the filtration of the water samples with a Cary 100 Varian spectrophotometer. The filters were scanned in the range 400–800 nm at intervals of 1 nm and the signal from a MilliQ water-saturated reference filter was subtracted automatically from the measurements of the OD. Total particulate absorption coefficients, ap(λ), were calculated as follows:

ap (λ ) =

ln(10) [0�3893 × [OD fp (λ) − OD null ] + 0�4340 × [OD fp (λ) − OD null ]2 ] V/A

where ODfp(λ) is the OD of the sample on the filter, OD null is the average OD of the sample in the range 780–800 nm, which was applied for the null point correction of the OD measurement, V is the volume of water filtered in cubic meters, and A is the area of the filter in square meters. The equation includes a quadratic function for the pathlength


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amplification correction of the measurements (Cleveland and Weidemann 1993) derived by Dall’Olmo (2006) for water samples from lakes and reservoirs in Nebraska and laboratory cultures of Microcystis and Synechococcus. Removal of the light absorption by pigments for the measurement of the absorption coefficient of nonalgal particles, aNAP(λ), followed the modified approach presented by Ferrari and Tassan (1999). The samples were treated with 120 μL sodium hypochlorite solution in MilliQ water (0.1%–0.2% active Cl) and rinsed with 50 mL MilliQ water after a 20-minute reaction time. The absorption coefficients of nonalgal particles were calculated similarly to the total particulate absorption coefficients. The absorption coefficients of phytoplankton, aϕ(λ), were finally calculated by the subtraction of the absorption coefficients of nonalgal particles from the particulate absorption coefficients. The absorption coefficients of CDOM were measured spectrophotmetrically immediately after the filtration of the water samples. The optical densities of the filtrates were measured in a 0.1-m cuvette in the range 200–800 nm at intervals of 1 nm with the Cary 100 Varian spectrophotometer and the signal from a MilliQ water reference sample was subtracted automatically from the measurements. Filtrates and MilliQ water reference samples were kept at 24°C (the temperature in the sample compartment of the instrument) to minimize the effects of a temperature-dependent water absorption feature at a wavelength of 750 nm. The absorption coefficients of CDOM, aCDOM(λ), were calculated by

aCDOM (λ ) =

ln(10) [OD s (λ) − OD null ] l

where ODs(λ) is the OD of the sample, ODnull is the average OD of the sample in the range 780–800 nm, which was applied for the null point correction, and l is the pathlength of the cuvette in meters. 18.4.3  Descriptive Statistics of Water Quality Parameters The descriptive statistics of Fremont Lakes water quality parameters indicate the typical range of variations for turbid productive inland, estuarine, and coastal waters (Tables 18.1 and 18.2) with chl-a concentrations that ranged from 2.3 to 200.8 mg ⋅ m−3 in 2008 and from 4.0 to 196.4 mg ⋅ m−3 in 2009. The distributions of the chl-a concentrations in 2008 and 2009 were significantly different from normal distributions (p